http://wiki.math.uwaterloo.ca/statwiki/api.php?action=feedcontributions&user=Aghabuss&feedformat=atomstatwiki - User contributions [US]2021-09-28T14:25:05ZUser contributionsMediaWiki 1.28.3http://wiki.math.uwaterloo.ca/statwiki/index.php?title=Attend_and_Predict:_Understanding_Gene_Regulation_by_Selective_Attention_on_Chromatin&diff=42343Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin2018-12-08T01:55:38Z<p>Aghabuss: /* HM-level Encoder (one LSTM) */</p>
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<div>This page contains a summary of the paper [https://arxiv.org/abs/1708.00339 "Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin."] by Singh, Ritambhara, et al. It was published at the Advances in Neural Information Processing Systems (NIPS) in 2017. The code for this paper is shared here[https://qdata.github.io/deep4biomed-web/].<br />
<br />
<br />
= Background =<br />
<br />
Gene regulation is the process of controlling which genes in a cell's DNA are turned 'on' (expressed) or 'off' (not expressed). By this process, a functional product such as a protein is created. Even though all the cells of a multicellular organism (e.g., humans) contain the same DNA, different types of cells in that organism may express very different sets of genes. As a result, each cell types have distinct functionality. In other words how a cell operates depends upon the genes expressed in that cell. Many factors including ‘Chromatin modification marks’ influence which genes are abundant in that cell.<br />
<br />
The function of chromatin is to efficiently wraps DNA around bead-like structures of histones into a condensed volume to fit into the nucleus of a cell, and protect the DNA structure and sequence during cell division and replication. Different chemical modifications in the histones of the chromatin, known as histone marks, change spatial arrangement of the condensed DNA structure. Which in turn affects the gene’s expression of the histone mark’s neighboring region. Histone marks can promote (obstruct) the gene to be turned on by making the gene region accessible (restricted). This section of the DNA, where histone marks can potentially have an impact, is known as DNA flanking region or ‘gene region’ which is considered to cover 10k base pair centered at the transcription start site (TSS) (i.e., a 5k base pair in each direction). Unlike genetic mutations, histone modifications are reversible [1]. Therefore, understanding the influence of histone marks in determining gene regulation can assist in developing drugs for genetic diseases.<br />
<br />
= Introduction = <br />
<br />
Revolution in genomic technologies now enables us to profile genome-wide chromatin mark signals. Therefore, biologists can now measure gene expressions and chromatin signals of the ‘gene region’ for different cell types covering whole human genome. The Roadmap Epigenome Project (REMC, publicly available) [2] recently released 2,804 genome-wide datasets of 100 separate “normal” (not diseased) human cells/tissues, among which 166 datasets are gene expression reads and the rest are signal reads of various histone marks. The goal is to understand which histone marks are the most important and how they interact together in gene regulation for each cell type.<br />
<br />
Signal reads for histone marks are high-dimensional and spatially structured. Influence of a histone modification mark can be anywhere in the gene region (covering 10k base pairs centered around the Transcription Start Site of each gene). It is important to understand how the impact of the mark on gene expression varies over the gene region. In other words, how histone signals over the gene region impacts the gene expression. There are different types of histone marks in human chromatin that can have an influence on gene regulation. Researchers have found five standard histone proteins. These five histone proteins can be altered in different combinations with different chemical modifications resulting in a large number of distinct histone modification marks. Different histone modification marks can act as a module to interact with each other and influence the gene expression.<br />
<br />
<br />
This paper proposes an attention-based deep learning model to find how this chromatin factors/ histone modification marks contributes to the gene expression of a particular cell. AttentiveChrome[3] utilizes a hierarchy of multiple LSTM to discover interactions between signals of each histone marks, and learn dependencies among the marks on expressing a gene. The authors included two levels of soft attention mechanism, (1) to attend to the most relevant signals of a histone mark, and (2) to attend to the important marks and their interactions. In this context, ''attention'' refers to weighting the importance of different items differently.<br />
<br />
== Main Contributions ==<br />
The contributions of this work can be summarized as follows:<br />
<br />
* More accurate predictions than the state-of-the-art baselines. This is measured using datasets from REMC on 56 different cell types.<br />
* Better interpretation than the state-of-the-art methods for visualizing deep learning model. They compute the correlation of the attention scores of the model with the mark signal from REMC. <br />
* Like the application of attention models previously in indirectly hinting the parts of the input that the model deemed important, AttentiveChrome can too explain it's decisions by hinting at “what” and “where” it has focused.<br />
* This is the first time that the attention based deep learning approach is applied to a problem in molecular biology.<br />
* Ability to deal with highly modular inputs<br />
<br />
= Previous Works = <br />
<br />
Machine learning algorithms to classify gene expression from histone modification signals have been surveyed by [15]. These algorithms vary from linear regression, support vector machine, and random forests to rule-based learning, and CNNs. To accommodate the spatially structured, high dimensional input data (histone modification signals) these studies applied different feature selection strategies. The preceding research study, DeepChrome [4], by the authors incorporated the best position selection strategy. The positions that are highly correlated to the gene expression are considered as the best positions. This model can learn the relationship between the histone marks. This CNN based DeepChrome model outperforms all the previous works. However, these approaches either (1) failed to model the spatial dependencies among the marks, or (2) required additional feature analysis. Only AttentiveChrome is reported to satisfy all of the eight desirable metrics of a model.<br />
<br />
= AttentiveChrome: Model Formulation =<br />
<br />
The authors proposed an end-to-end architecture which has the ability to simultaneously attend and predict. This method incorporates recurrent neural networks (RNN) composed of LSTM units to model the sequential spatial dependencies of the gene regions and predict gene expression level from The embedding vector, <math> h_t </math>, output of an LSTM module encodes the learned representation of the feature dependencies from the time step 0 to <math> t </math>. For this task, each bin position of the gene region is considered as a time step.<br />
<br />
The proposed AttentiveChrome framework contains following 5 important modules:<br />
<br />
* Bin-level LSTM encoder encoding the bin positions of the gene region (one for each HM mark)<br />
* Bin-level <math> \alpha </math>-Attention across all bin positions (one for each HM mark)<br />
* HM-level LSTM encoder (one encoder encoding all HM marks)<br />
* HM-level <math> \beta </math>-Attention among all HM marks (one)<br />
* The final classification module<br />
<br />
Figure 1 (Supplementary Figure 2) presents an overview of the proposed AttentiveChrome framework.<br />
<br />
<br />
[[File:supplemntary_figure_2.png|thumb|center| 800px |Figure 1: Overview of the all five modules of the proposed AttentiveChrome framework]]<br />
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<br />
<br />
== Input and Output ==<br />
<br />
Each dataset contains the gene expression labels and the histone signal reads for one specific cell type. The authors evaluated AttentiveChrome on 56 different cell types. For each mark, we have a feature/input vector containing the signals reads surrounding the gene’s TSS position (gene region) for the histone mark. The label of this input vector denotes the gene expression of the specific gene. This study considers binary labeling where <math> +1 </math> denotes gene is expressed (on) and <math> -1 </math> denotes that the gene is not expressed (off). Each histone marks will have one feature vector for each gene. The authors integrates the feature inputs and outputs of their previous work DeepChrome [4] into this research. The input feature is represented by a matrix <math> \textbf{X} </math> of size <math> M \times T </math>, where <math> M </math> is the number of HM marks considered in the input, and <math> T </math> is the number of bin positions taken into account to represent the gene region. The <math> j^{th} </math> row of the vector <math> \textbf{X} </math>, <math> x_j</math>, represents sequentially structured signals from the <math> j^{th} </math> HM mark, where <math> j\in \{1, \cdots, M\} </math>. Therefore, <math> x_j^t</math>, in the matrix <math> \textbf{X} </math> represents the value from the <math> t^{th}</math> bin belonging to the <math> j^{th} </math> HM mark, where <math> t\in \{1, \cdots, T\} </math>. If the training set contains <math>N_{tr} </math> labeled pairs, the <math> n^{th} </math> is specified as <math>( X^n, y^n)</math>, where <math> X^n </math> is a matrix of size <math> M \times T </math> and <math> y^n \in \{ -1, +1 \} </math> is the binary label, and <math> n \in \{ 1, \cdots, N_{tr} \} </math>.<br />
<br />
Figure 2 (also refer to Figure 1 (a), and 1(b) for better understanding) exhibits the input feature, and the output of AttentiveChrome for a particular gene (one sample).<br />
<br />
[[File:input-output-attentivechrome.png|center|thumb| 700px | Figure 2: Input and Output of the AttentiveChrome model]]<br />
<br />
== Bin-Level Encoder (one LSTM for each HM) ==<br />
The sequentially ordered elements (each element actually is a bin position) of the gene region of <math> n^{th} </math> gene is represented by the <math> j_{th} </math> row vector <math> x^j </math>. The authors considered each bin position as a time step for LSTM. This study incorporates bidirectional LSTM to model the overall dependencies among a total of <math> T </math> bin positions in the gene region. The bidirectional LSTM contains two LSTMs<br />
* A forward LSTM, <math> \overrightarrow{LSTM_j} </math>, to model <math> x^j </math> from <math> x_1^j </math> to <math> x_T^j </math>, which outputs the embedding vector <math> \overrightarrow{h^t_j} </math>, of size <math> d </math> for each bin <math> t </math><br />
* A reverse LSTM, <math> \overleftarrow{LSTM_j} </math>, to model <math> x^j </math> from <math> x_T^j </math> to <math> x_1^j </math>, which outputs the embedding vector <math> \overleftarrow{h^j_t} </math>, of size <math> d </math> for each bin <math> t </math><br />
<br />
The final output of this layer, embedding vector at <math> t^{th} </math> bin for the <math> j^{th} </math> HM, <math> h^j_t </math>, of size <math> d </math>, is obtained by concatenating the two vectors from the both directions. Therefore, <math> h^j_t = [ \overrightarrow{h^j_t}, \overleftarrow{h^j_t}]</math>. By pairing these LSTM-based HM encoders with the final classification, embedding each HM mark by drawing out the dependencies among bins can be learned by these pairs.Figure 1 (c) illustrates the module for <math> j=2 </math>.<br />
<br />
== Bin-Level <math> \alpha</math>-attention ==<br />
<br />
Each bin contributes differently in the encoding of the entire <math> j^{th} </math> mark. To automatically and adaptively highlight the most important bins for prediction, a soft attention weight vector <math> \alpha^j </math> of size <math> T </math> is learned for each <math> j </math>. To calculated the soft weight <math> \alpha^j_t </math>, for each <math> t </math>, the embedding vectors <math> \{h^j_1, \cdots, h^j_t \} </math> of all the bins are utilized. The following equation is used:<br />
<br />
<center><math> \alpha^j_t = \frac{exp(\textbf{W}_b h^j_t)}{\sum_{i=1}^T{exp(\textbf{W}_b h^j_i)}} </math></center><br />
<br />
<br />
<math> \alpha^j_t</math> is a scalar and is computed by all bins’ embedding vectors <math>h^j</math>. The parameter <math> W_b </math> is initialized randomly, and learned alongside during the process with the other model parameters. Therefore, once we have importance weight of each bin position, the <math> j^{th} </math> HM mark can be represented by <math> m^j = \sum_{t=1}^T{\alpha^j_t \times h^j_t}</math>. Here, <math> h^j_t</math> is the embedding vector and <math> \alpha^t_j </math> is the importance weight of the <math> t^{th} </math> bin in the representation of the <math> j^{th} </math> HM mark. Intuitively <math> \textbf{W}_b </math> will learn the cell type. Figure 1(d) shows this module for <math> HM_2 </math>.<br />
<br />
== HM-level Encoder (one LSTM) ==<br />
<br />
Studies observed that HMs work cooperatively to provoke or subdue gene expression [5]. The HM-level encoder (not in the fFgure 1) utilizes one bidirectional LSTM to capture this relationship between the HMs. To formulate the sequential dependency a random sequence is imagined as the authors did not find influence of any specific ordering of the HMs. The representation <math> m_j </math>of the <math> j^{th} </math> HM, <math> HM_j </math>, which is calculated from the bin-level attention layer, is the input of this step. This set based encoder outputs an embedding vector <math> s^j </math> of size <math> d’ </math>, which is the encoding for the <math> j^{th} </math> HM.<br />
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<math> s^j = [ \overrightarrow{LSTM_s}(m_j), \overleftarrow{LSTM_s}(m_j) ] </math><br />
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The dependencies between <math> j^{th} </math> HM and the other HM marks are encoded in <math> s^j </math>, whereas <math> m^j </math> from the previous step encodes the bin dependencies of the <math> j^{th} </math> HM.<br />
<br />
[[File:table1.png|center|thumb| 700px | Table 1: Comparison of previous studies for the task of quantifying gene expression using histonemodification marks. AttentiveChrome is the only model that exhibits all 8desirable properties.]]<br />
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== HM-Level <math> \beta</math>-attention ==<br />
This second soft attention level (Figure 1(e)) finds the important HM marks for classifying a gene’s expression by learning the importance weights, <math> \beta_j </math>, for each <math> HM_j </math>, where <math> j \in \{ 1, \cdots, M \} </math>. The equation is <br />
<br />
<math> \beta^j = \frac{exp(\textbf{W}_s s^j)}{\sum_{i=1}^M{exp(\textbf{W}_s s^j)}} </math><br />
<br />
The HM-level context parameter <math> \textbf{W}_s </math> is trained jointly in the process. Intuitively <math> \textbf{W}_s </math> learns how the HMs are significant for a cell type. Finally the entire gene region is encoded in a hidden representation <math> \textbf{v} </math>, using the weighted sum of the embedding of all HM marks. <br />
<br />
<br />
<math> \textbf{v} = \sum_{j=1}^MT{\beta^j \times s^j}</math><br />
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== End-to-end training ==<br />
<br />
The embedding vector <math> \textbf{v} </math> is fed to a simple classification module, <math> f(\textbf{v}) = </math>softmax<math> (\textbf{W}_c\textbf{v}+b_c) </math>, where <math> \textbf{W}_c </math>, and <math> b_c </math> are learnable parameters. The output is the probability of gene expression being high (expressed) or low (suppressed).<br />
The whole model including the attention modules is differentiable. Thus backpropagation can perform end-to-end learning trivially. The negative log-likelihood loss function is minimized in the learning.<br />
<br />
= Experimental Settings =<br />
<br />
This work makes use of the REMC dataset. AttentiveChrome is evaluated on 56 different cell types. Similar to DeepChrome, this study considered the following five core HM marks (<math> M=5 </math>). Because these selected marks are uniformly profiled across all 56 cell types in the REMC study.<br />
<br />
[[File:HM.png|center|thumb| 700px | Table 1: Five core HM marks and their attributes considered in this paper]]<br />
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<br />
<br />
For a gene region 10k base pairs centred at the TSS site (5k bp in each direction) are taken into account. These 10k base pairs are divided into 100 bins, each bin consisting of <math> T=100 </math> continuous bp). Therefore, for each gene in a particular cell type, the input matrix will be of size <math> 5 \times 100 </math>. The gene expression labels are normalized and discretized to represent binary labelling. The sample dataset is divided into three equal sized folds for training, validation, and testing.<br />
<br />
== Model Variations and Two Baselines ==<br />
To evaluate the performance of the proposed model the authors considered RNN method (direct LSTM without any attention), and their prior work DeepChrome as baselines. The results obtained from multiple variations of the AttentiveChrome model are compared with the baselines. The authors considered five variant of AttentiveChrome during performance evaluation. The variants are:<br />
<br />
* LSTM-Attn: one LSTM with attention on the input matrix (does not consider the modular nature of HM marks)<br />
* CNN-Attn: DeepChrome [4] with one attention mechanism incorporated. <br />
* LSTM-<math>\alpha , \beta</math>: the proposed architecture.<br />
* CNN-<math>\alpha , \beta</math>: LSTM module of the proposed architecture replaced with CNN. This variation includes two attention mechanisms. First attention mechanism contains one <math>\alpha</math>-attention on top of a CNN module per HM mark. And, the second -<math>\beta</math>- attention mechanism is used to combine HMs.<br />
* LSTM-<math>\alpha</math>: one LSTM and <math>\alpha</math>-attention per HM mark.<br />
<br />
== Hyperparameters ==<br />
<br />
For all the variants of AttentiveChrome the bin-level LSTM embedding size <math> d</math> is set to 32, and the HM-level LSTM embedding size <math>d’</math> is set to 16. Because of bidirectional LSTM, the size of the embedding vector <math> h_t</math>, and <math>m_j</math> will be 64, and 32 respectively. Size of the context vectors are set accordingly.<br />
<br />
= Performance Evaluation =<br />
<br />
== AUC Scores ==<br />
<br />
This study summarizes AUC scores across all 56 cell types on the test set to compare the methods.<br />
<br />
[[File:AUC.JPG|center|thumb| 700px | Table 2: AUC score performances for different variations of AttentiveChrome and baselines]]<br />
<br />
Overall the LSTM-attention models perform better than the DeepChrome (CNN-based) and LSTM baselines. The authors argue that the proposed AttentiveChrome model is a good choice because of its interpretability, even though the performance improvement from DeepChrome is insignificant.<br />
<br />
== Evaluation of Attention Scores for Interpretation ==<br />
<br />
To understand if the model is focusing on the right regions, the authors make use of additional study results from REMC database. To validate the bin attention,signal data of a new histone mark, H3K27ac, referred to as <math>H_{active}</math> in this article, from REMC database is utilized. This particular histone mark is known to mark active region when the gene is expressed (ON). Genome-wide read of this HM mark is available for three important cell types: stem cell (H1-hESC), blood cell (GM12878), and leukemia cell (K562). This particular HM mark is used to analyze the visualization results only and not applied in the learning phase. The authors discussed performance of both the attention mechanisms in this section. <br />
<br />
=== Correlation of Importance Weight of <math>H_{prom}</math> with <math>H_{active}</math> ===<br />
<br />
Average read count of <math>H_{active}</math> across all 100 bins for all the active genes (ON or labeled as <math>+1</math>) in the three selected cell types is calculated. The proposed AttentiveChrome and LSTM-<math>\alpha</math> methods are compared with two widely used visualization techniques, (1) class based, and (2) saliency map applied on the baseline DeepChrome model (CNN-based prior work). Using these visualization methods, the authors calculate the importance weights for <math>H_{prom}</math> (promoter HM mark used in training) across the 100 bins. The Pearson Correlation score between these importance weights and the read count of the <math>H_{active}</math> (HM mark for validation) across the same 100 bins is computed. The <math>H_{active}</math> read counts indicates the actual active regions of those cells. <br />
<br />
[[File: pc.JPG|center|thumb| 700px | Figure 4: Pearson Correlation between a known active HM mark]]<br />
<br />
<br />
The results indicate that the proposed models consistently gained highest correlation with <math>H_{active}</math> for all three cell types. Thus, the proposed method is successful to capture the important signals.<br />
<br />
=== Visualization of Attention Weight of bins for each HM of a specific cell type GM12878===<br />
<br />
To visualize bin level attention weights, the authors plotted the average bin-level attention weights for each HM for a specific cell type GM12878 (blood cell) for expressed (ON) genes and suppressed (OFF) genes separately. <br />
<br />
[[File: figure2.png|center|thumb| 700px |]]<br />
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For the “ON” genes, the attention profiles are well defined for the HM marks, <math>H_{prom}</math>, <math>H_{enhc}</math>, <math>H_{struct}</math>. On the other hand, the weights are low for <math>H_{reprA}</math> and <math>H_{reprB}</math>. The average trend reverses for the “OFF” genes, where the repressor HM marks have more influence than the <math>H_{prom}</math>, <math>H_{enhc}</math>, <math>H_{struct}</math>. This observation agrees with the biologist finding that <math>H_{prom}</math>, <math>H_{enhc}</math>, <math>H_{struct}</math> marks stimulates gene activation and, <math>H_{reprA}</math> and <math>H_{reprB}</math> mark restrains the genes.<br />
<br />
=== Attention Weight of bins with <math>H_{active}</math>===<br />
<br />
The average read counts of <math>H_{active}</math> for the same 100 bins across all the active (ON) genes for the cell type GM12878 is plotted (FIGURE 2(b)). Besides, for AttentiveChrome the plot of bin-level attention weights of averaged over all the genes that are PREDICTED ON for GM12878 is also provided. The plots exhibit that the <math>H_{prom}</math> profile is similar to <math>H_{active}</math>.<br />
<br />
=== Visualization of HM-level Attention Weight for Gene PAX5 ===<br />
<br />
To visualize HM-level attention weight the authors produces a heatmap for a differentially regulated gene, PAX5, for the three aforementioned cell types. The heatmap is presented in FIGURE 2(c). PAX5 plays significant role in gene regulation when stem cells convert to blood cells. This gene is OFF in stem cells (H1-hESC), however it becomes activated when the stem cell is transformed into blood cell (GM12878). The <math>\beta_j</math> weight for <math>H_{repr}</math> is high when the gene is OFF in H1-hESC, and the weight decreases when the gene is ON in GM12878. On the contrary, for <math>H_{prom}</math> mark the <math>\beta_j</math> weight increases from H1-hESC to GM12878 as the gene becomes activated. This information extracted by the deep learning model is also supported by biological literature [16].<br />
<br />
= Related Works/Studies =<br />
<br />
In the last few years, deep learning models obtained models obtained unprecedented success in diverse research fields. Though as not rapidly as other fields, deep learning based algorithms are gaining popularity among bioinformaticians.<br />
<br />
== Attention-based Deep Models ==<br />
<br />
The idea of attention technique in deep learning is adapted from the human visual perception system. Humans tend to focus over some parts more than the others while perceiving a scene. This mechanism augmented with deep neural networks achieved an excellent outcome in several research topics, such as machine translation. Various types of attention models e.g., soft [6], or location-aware [7], or hard [8, 9] attentions have been proposed in the literature. In the soft attention model, a soft weight vector is calculated for the overall feature vectors. The extent of the weight is correlated with the degree of importance of the feature in the prediction. In practice, RNN is often used to help implement such models.<br />
<br />
== Visualization and Apprehension of Deep Models ==<br />
<br />
Prior studies mostly focused on interpreting convolutional neural networks (CNN) for image classification. Deconvulation approaches [10] attempt to map hidden layer representations back to an input space. Saliency maps [11, 12], attempt to use taylor expansion to approximate the network, and identify the most relevant input features. Class optimization [12] based visualization techniques attempt to find the best example member of each class. Some recent research works [13, 14] tried to understand recurrent neural networks (RNN) for text-based problems. By looking into the features the model attends to, we can interpret the output of a deep model.<br />
<br />
== Deep Learning in Bioinformatics ==<br />
Deep learning is also getting popular in bioinformatics fields because it is able to extract meaningful representations from datasets. Scholars use deep learning to model protein sequences and DNA sequences and predicting gene expressions.<br />
<br />
== Previous model for gene expression predictions ==<br />
There were multiple machine learning models had been used to predict gene expressions from histone modification data (surveyed in [19]), such as linear regression[21], random forests[18], rule-based learning [19] and CNNs [22] and support vector machines[17].These studies designed different feature selection strategies to accommodate a large amount of histone modification signals as input. The strategies included using signal averaging across all relevant positions and selecting input signals at positions where was highly correlated to target gene expression and then use CNN (called DeepChrome [22]) to learn combinatorial interactions among histone modification marks. DeepChrome outperformed all previous methods (see Supplementary) on this task and used a class optimization-based technique for visualizing the learned model. However, this class-level visualization lacks the necessary granularity to understand the signals from multiple chromatin marks at the individual gene level.<br />
<br />
= Conclusion = <br />
<br />
The paper has introduced an attention-based approach called "AttentiveChrome" that deals with both understanding and prediction with several advantages on previous architectures including higher accuracy from state-of-the-art baselines, clearer interpretation than saliency map, which allows them to view what the model ‘sees’ during prediction, and class optimization. Another advantage of this approach is that it can model modular feature inputs which are sequentially structured. Finally, according to the authors, this is the first implementation of deep attention to understand gene regulation. AttentiveChrome is claimed to be the first attention based model applied on a molecular biology dataset. The authors expect that through this deep attention mechanism, the biologists can have a better understanding of epigenomic data. It can model feature inputs that are sequentially structured. This model can handle understanding and prediction of hard to interpret biological data as it grants insights<br />
to the predictions by locating ‘what’ and ‘where’ AttentiveChrome has focused.<br />
<br />
= Critiques =<br />
<br />
This paper does not give a considerable algorithmic contribution. They have only used existing methods for this application. This deep learning based method is shown to perform better than simple machine learning models like linear regression and SVMs but this is considerably harder to implement and has many more hyperparameters to tune. The training time is considerably higher, especially because all the parameters are learned together. The dataset considered in the application here also seems to have only a limited number of samples for a study of high complexity. Model hyperparameters have been chosen randomly without any explanation of intuition for them. The authors have also not cited any relevant literature to understand where these numbers came from. <br />
<br />
Discussion about attention scores for interpretation does not provide any clear definition or mention previous literature using them. Reference of literature about H3K27ac, and how its read counts represent active region of a cell should be included. No reasoning given for why only one specific cell type is used to visualize bin level attention weights. Example of some other real world problems where this model can be useful should be provided.<br />
<br />
Moreover, this paper relies heavily on the intuition. Due to complicated structures, it must be challenging to provide algorithmic/theoretical justifications. This means that there is no proper guidence of how hyperparameters should be chosen or any kinds of treatment that the author performs on other data sets.<br />
<br />
= Additional Resources =<br />
<br />
# [https://qdata.github.io/deep4biomed-web/ Official DeepChrome Website]<br />
# [http://papers.nips.cc/paper/7255-attend-and-predict-understanding-gene-regulation-by-selective-attention-on-chromatin-supplemental.zip Supplemental Resources]<br />
# [https://github.com/QData/AttentiveChrome/blob/master/NIPS%20poster.pdf Poster]<br />
# [https://www.youtube.com/watch?v=tfgmXvSgsQE&feature=youtu.be Video Presentation]<br />
<br />
= Reference =<br />
<br />
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<br />
[2] Anshul Kundaje, Wouter Meuleman, Jason Ernst, Misha Bilenky, Angela Yen, Alireza Heravi-Moussavi, Pouya Kheradpour, Zhizhuo Zhang, Jianrong Wang, Michael J Ziller, et al. Integrative analysis of 111 reference human epigenomes. Nature, 518(7539):317–330, 2015.<br />
<br />
[3] Singh, Ritambhara, et al. "Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin." Advances in Neural Information Processing Systems. 2017.<br />
<br />
[4] Ritambhara Singh, Jack Lanchantin, Gabriel Robins, and Yanjun Qi. Deepchrome: deep-learning for predicting gene expression from histone modifications. Bioinformatics, 32(17):i639–i648, 2016.<br />
<br />
[5] Joanna Boros, Nausica Arnoult, Vincent Stroobant, Jean-François Collet, and Anabelle Decottignies. Polycomb repressive complex 2 and h3k27me3 cooperate with h3k9 methylation to maintain heterochromatin protein 1α at chromatin. Molecular and cellular biology, 34(19):3662–3674, 2014.<br />
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[6] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473, 2014.<br />
<br />
[7] Jan K Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, Kyunghyun Cho, and Yoshua Bengio. Attention-based models for speech recognition. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems 28, pages 577–585. Curran Associates, Inc., 2015.<br />
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[8] Minh-Thang Luong, Hieu Pham, and Christopher D. Manning. Effective approaches to attention-based neural machine translation. In Empirical Methods in Natural Language Processing (EMNLP), pages 1412–1421, Lisbon, Portugal, September 2015. Association for Computational Linguistics.<br />
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[9] Huijuan Xu and Kate Saenko. Ask, attend and answer: Exploring question-guided spatial attention for visual question answering. In ECCV, 2016.<br />
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[10] Matthew D Zeiler and Rob Fergus. Visualizing and understanding convolutional networks. In Computer Vision–ECCV 2014, pages 818–833. Springer, 2014.<br />
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[11] David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert MÃžller. How to explain individual classification decisions. volume 11, pages 1803–1831, 2010.<br />
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[12] Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. Deep inside convolutional networks: Visualising image classification models and saliency maps. 2013.<br />
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[13] Andrej Karpathy, Justin Johnson, and Fei-Fei Li. Visualizing and understanding recurrent networks. 2015.<br />
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[14] Jiwei Li, Xinlei Chen, Eduard Hovy, and Dan Jurafsky. Visualizing and understanding neural models in nlp. 2015.<br />
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[15] Xianjun Dong and Zhiping Weng. The correlation between histone modifications and gene expression. Epigenomics, 5(2):113–116, 2013.<br />
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[16] Shane McManus, Anja Ebert, Giorgia Salvagiotto, Jasna Medvedovic, Qiong Sun, Ido Tamir, Markus Jaritz, Hiromi Tagoh, and Meinrad Busslinger. The transcription factor pax5 regulates its target genes by recruiting chromatin-modifying proteins in committed b cells. The EMBO journal, 30(12):2388–2404, 2011.<br />
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[17] ChaoCheng,Koon-KiuYan,KevinYYip,JoelRozowsky,RogerAlexander,ChongShou,MarkGerstein, et al. A statistical framework for modeling gene expression using chromatin features and application to modencode datasets. Genome Biol, 12(2):R15, 2011.<br />
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[18] XianjunDong,MelissaCGreven,AnshulKundaje,SarahDjebali,JamesBBrown,ChaoCheng,ThomasR Gingeras, Mark Gerstein, Roderic Guigó, Ewan Birney, et al. Modeling gene expression using chromatin features in various cellular contexts. Genome Biol, 13(9):R53, 2012.<br />
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[19] Xianjun Dong and Zhiping Weng. The correlation between histone modifications and gene expression. Epigenomics, 5(2):113–116, 2013.<br />
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[20] Bich Hai Ho, Rania Mohammed Kotb Hassen, and Ngoc Tu Le. Combinatorial roles of dna methylation and histone modifications on gene expression. In Some Current Advanced Researches on Information and Computer Science in Vietnam, pages 123–135. Springer, 2015.<br />
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[21] Rosa Karlic ́, Ho-Ryun Chung, Julia Lasserre, Kristian Vlahovicˇek, and Martin Vingron. Histone mod- ification levels are predictive for gene expression. Proceedings of the National Academy of Sciences, 107(7):2926–2931, 2010.<br />
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[22] Ritambhara Singh, Jack Lanchantin, Gabriel Robins, and Yanjun Qi. Deepchrome: deep-learning for predicting gene expression from histone modifications. Bioinformatics, 32(17):i639–i648, 2016.</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:table1.png&diff=42342File:table1.png2018-12-08T01:53:49Z<p>Aghabuss: Aghabuss uploaded a new version of File:table1.png</p>
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<div></div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=MULTI-VIEW_DATA_GENERATION_WITHOUT_VIEW_SUPERVISION&diff=42341MULTI-VIEW DATA GENERATION WITHOUT VIEW SUPERVISION2018-12-08T01:51:05Z<p>Aghabuss: /* Evaluation of the Quality of Generated Samples */</p>
<hr />
<div>This page contains a summary of the paper "[https://openreview.net/forum?id=ryRh0bb0Z Multi-View Data Generation without Supervision]" by Mickael Chen, Ludovic Denoyer, Thierry Artieres. It was published at the International Conference on Learning Representations (ICLR) in 2018. An implementation of the models presented in this paper is available here[https://github.com/mickaelChen/GMV]<br />
<br />
==Introduction==<br />
<br />
===Motivation===<br />
High Dimensional Generative models have seen a surge of interest of late with the introduction of Variational Auto-Encoders and Generative Adversarial Networks. This paper focuses on a particular problem where one aims at generating samples corresponding to a number of objects under various views. The distribution of the data is assumed to be driven by two independent latent factors: the content, which represents the intrinsic features of an object, and the view, which stands for the settings of a particular observation of that object (for example, the different angles of the same object). The paper proposes two models using this disentanglement of latent space - a generative model and a conditional variant of the same. The authors claim that unlike many multiview<br />
approaches, the proposed model doesn’t need any supervision on the views but only on the content.<br />
<br />
===Related Work===<br />
<br />
The problem of handling multi-view inputs has mainly been studied from the predictive point of view where one wants, for example, to learn a model able to predict/classify over multiple views of the same object (Su et al. (2015); Qi et al. (2016)). These approaches generally involve (early or late) fusion of the different views at a particular level of a deep architecture. Recent studies have focused on identifying factors of variations from multiview datasets. The underlying idea is to consider that a particular data sample may be thought as the mix of a content information (e.g. related to its class label like a given person in a face dataset) and of a side information, the view, which accounts for factors of variability (e.g. exposure, viewpoint, with/wo glasses...). So, all the samples of the same class contain the same content but different view. A number of approaches have been proposed to disentangle the content from the view (i.e. methods based on unlabeled samples), also referred as the style in some papers (Mathieu et al. (2016); Denton & Birodkar (2017)). The two common limitations the earlier approaches pose - as claimed by the paper - are that (i) they usually<br />
consider discrete views that are characterized by a domain or a set of discrete (binary/categorical) attributes (e.g. face with/wo glasses, the color of the hair, etc.) and could not easily scale to a large number of attributes or to continuous views. (ii) most models are trained using view supervision (e.g. the view attributes), which of course greatly helps in the learning of such model, yet prevents their use on many datasets where this information is not available.<br />
<br />
Recently such attempts have been made to learn such models without supervision, but they cannot disentangle high level concepts as only simple features can be reliably captured without any guidance.<br />
<br />
===Contributions===<br />
<br />
The contributions that authors claim are the following: (i) A new generative model able to generate data with various content and high view diversity using a supervision on the content information only. (ii) Extend the generative model to a conditional model that allows generating new views over any input sample. (iii) Report experimental results on four different images datasets to prove that the models can generate realistic samples and capture (and generate with) the diversity of views.<br />
<br />
Precisely,two models have been proposed:<br />
# a generative model ('''GMV - Generative Multi-view Model''') that generates objects under various views (multiview generation), <br />
# and a conditional extension, '''conditional GMV (C-GMV)''' of this model that generates a large number of views of any input object (conditional multi-view generation). <br />
<br />
Both models are based on the adversarial training schema of Generative Adversarial Networks (GAN) proposed in Goodfellow et al. (2014)). The simple but strong idea is to focus on distributions over pairs of examples (e.g. images representing a same object in different views) rather than distribution on single examples.<br />
<br />
==Paper Overview==<br />
<br />
===Background===<br />
<br />
The paper uses the concept of the popular GAN (Generative Adverserial Networks) proposed by Goodfellow et al.(2014).<br />
<br />
GENERATIVE ADVERSARIAL NETWORK:<br />
<br />
Generative adversarial networks (GANs) are deep neural net architectures comprised of two nets, pitting one against the other (thus the “adversarial”). GANs was introduced in a paper by Ian Goodfellow and other researchers at the University of Montreal, including Yoshua Bengio, in 2014. Referring to GANs, Facebook’s AI research director Yann LeCun called adversarial training “the most interesting idea in the last 10 years in ML.”<br />
<br />
Let us denote <math>X</math> an input space composed of multidimensional samples <math>x</math> e.g. vector, matrix or tensor. Given a latent space <math>R^n</math> and a prior distribution <math>p_z(z)</math> over this latent space, any generator function <math>G : R^n → X</math> defines a distribution <math>p_G </math> on <math> X</math> which is the distribution of samples <math>G(z)</math> where <math>z ∼ p_z</math>. A GAN defines, in addition to <math>G</math>, a discriminator function <math>D : X → [0; 1]</math> which aims at differentiating between real inputs sampled from the training set and fake inputs sampled from <math>p_G</math>, while the generator learns to fool the discriminator <math>D</math>. Usually both <math>G</math> and <math>D</math> are implemented with neural networks. The objective function is based on the following adversarial criterion:<br />
<br />
<div style="text-align: center;font-size:100%"><math>\underset{G}{min} \ \underset{D}{max}</math> <math>E_{p_x}[log D(x)] + Ep_z[log(1 − D(G(z)))]</math></div><br />
<br />
where <math>p_x</math> is the empirical data distribution on <math>X</math> .<br />
It has been shown in Goodfellow et al. (2014) that if G∗ and D∗ are optimal for the above criterion, the Jensen-Shannon divergence between <math>p_{G∗}</math> and the empirical distribution of the data <math>p_x</math> in the dataset is minimized, making GAN able to estimate complex continuous data distributions.<br />
<br />
CONDITIONAL GENERATIVE ADVERSARIAL NETWORK:<br />
<br />
In the Conditional GAN (CGAN), the generator learns to generate a fake sample with a specific condition or characteristics (such as a label associated with an image or more detailed tag) rather than a generic sample from unknown noise distribution. The conditionality of a CGAN is determined by defining a generator function <math>G</math> which takes a noise vector <math>z</math> and a condition <math>y</math> as inputs. Now, to add such a condition to both generator and discriminator, we will simply feed some vector <math>y</math>, into both networks. Hence, both the discriminator <math>D(X,y)</math> and generator <math>G(z,y)</math> are jointly distributed with <math>y</math>. A target <math>X</math> from a given input <math>y</math> can be obtained by first sampling the latent vector <math>z ∼ p_z</math>, then by computing <math>G(y, z)</math>. The discriminator takes both the condition <math>y</math> and the datapoint <math>x</math> as inputs.<br />
<br />
Now, the objective function of CGAN is:<br />
<br />
<div style="text-align: center;font-size:100%"><math>\underset{G}{min} \ \underset{D}{max}</math> <math>E_{p_x}[log D(x,y)] + Ep_z[log(1 − D(G(y,z)))]</math></div><br />
<br />
The paper also suggests that many studies have reported that when dealing with high-dimensional input spaces, CGAN tends to collapse the modes of the data distribution, mostly ignoring the latent factor <math>z</math> and generating <math>x</math> only based on the condition <math>y</math>, exhibiting an almost deterministic behavior. At this point, the CGAN also fails to produce a satisfying amount of diversity in generated samples.<br />
<br />
===Generative Multi-View Model===<br />
<br />
''' Objective and Notations: ''' The distribution of the data x ∈ X is assumed to be driven by two latent factors: a content factor denoted c which corresponds to the invariant proprieties of the object and a view factor denoted v which corresponds to the factor of variations. Typically, if X is the space of people’s faces, c stands for the intrinsic features of a person’s face while v stands for the transient features and the viewpoint of a particular photo of the face, including the photo exposure<br />
and additional elements like a hat, glasses, etc.... These two factors c and v are assumed to be independent and these are the factors needed to learn.<br />
<br />
The paper defines two tasks here to be done: <br />
(i) '''Multi View Generation''': we want to be able to sample over X by controlling the two factors c and v. Given two priors, p(c) and p(v), this sampling will be possible if we are able to estimate p(x|c, v) from a training set.<br />
(ii) '''Conditional Multi-View Generation''': the second objective is to be able to sample different views of a given object. Given a prior p(v), this sampling will be achieved by learning the probability p(c|x), in addition to p(x|c, v). Ability to learn generative models able to generate from a disentangled latent space would allow controlling the sampling on the two different axes,<br />
the content and the view. The authors claim the originality of work is to learn such generative models without using any view labeling information.<br />
<br />
The paper introduces the vectors '''c''' and '''v''' to represent latent vectors in R<sup>c</sup> and R<sup>v</sup><br />
<br />
<br />
''' Generative Multi-view Model: '''<br />
<br />
Consider two prior distributions over the content and view factors denoted as <math>p_c</math> and <math>p_v</math>, corresponding to the prior distribution over content and latent factors. Moreover, we consider a generator G that implements a distribution over samples x, denoted as <math>p_G</math> by computing G(c, v) with <math>c ∼ p_c</math> and <math>v ∼ p_v</math>. The objective is to learn this generator so that its first input c corresponds to the content of the generated sample while its second input v, captures the underlying view of the sample. Doing so would allow one to control the output sample of the generator by tuning its content or its view (i.e. c and v).<br />
<br />
The key idea that authors propose is to focus on the distribution of pairs of inputs rather than on the distribution over individual samples. When no view supervision is available the only valuable pairs of samples that one may build from the dataset consist of two samples of a given object under two different views. When we choose any two samples randomly from the dataset from the same object, it is most likely that we get two different views. The paper explains that there are three goals here, (i) As in regular GAN, each sample generated by G needs to look realistic. (ii) As real pairs are composed of two views of the same object, the generator should generate pairs of the same object. Since the two sampled view factors v1 and v2 are different, the only way this can be achieved is by encoding the content vector c which is invariant. (iii) It is expected that the discriminator should easily discriminate between a pair of samples corresponding to the same object under different views from a pair of samples corresponding to a same object under the same view. Because the pair shares the same content factor c, this should force the generator to use the view factors v1 and v2 to produce diversity in the generated pair.<br />
<br />
Now, the objective function of GMV Model is:<br />
<br />
<div style="text-align: center;font-size:100%"><math>\underset{G}{min} \ \underset{D}{max}</math> <math>E_{x_1,x_2}[log D(x_1,x_2)] + E_{v_1,v_2}[log(1 − D(G(c,v_1),G(c,v_2)))]</math></div><br />
<br />
Once the model is learned, generator G that generates single samples by first sampling c and v following <math>p_c</math> and <math>p_v</math>, then by computing G(c, v). By freezing c or v, one may then generate samples corresponding to multiple views of any particular content, or corresponding to many contents under a particular view. One can also make interpolations between two given views over a particular content, or between two contents using a particular view<br />
<br />
<div style="text-align: center;font-size:100%">[[File:GMV.png]]</div><br />
<br />
===Conditional Generative Model (C-GMV)===<br />
<br />
C-GMV is proposed by the authors to be able to change the view of a given object that would be provided as an input to the model. This model extends the generative model's the ability to extract the content factor from any given input and to use this extracted content in order to generate new views of the corresponding object. To achieve such a goal, we must add to our generative model an encoder function denoted <math>E : X → R^C</math> that will map any input in X to the content space <math>R^C</math><br />
<br />
Input sample x is encoded in the content space using an encoder function, noted E (implemented as a neural network).<br />
This encoder serves to generate a content vector c = E(x) that will be combined with a randomly sampled view <math>v ∼ p_v</math> to generate an artificial example. The artificial sample is then combined with the original input x to form a negative pair. The issue with this approach is that CGAN is known to easily miss modes of the underlying distribution. The generator enters in a state where it ignores the noisy component v. To overcome this phenomenon, we use the same idea as in GMV. We build negative pairs <math>(G(c, v_1), G(c, v_2))</math> by randomly sampling two views <math>v_1</math> and <math>v_2</math> that are combined to get a unique content c. c is computed from a sample x using the encoder E, i.e. c= E(x). By doing so, the ability of our approach to generating pairs with view diversity is preserved. Since this diversity can only be captured by taking into account the two different view vectors provided to the model (<math>v_1</math> and <math>v_2</math>), this will encourage G(c, v) to generate samples containing both the content information c, and the view v. Positive pairs are sampled from the training set and correspond to two views of a given object.<br />
<br />
The Objective function for C-GMV will be:<br />
<br />
<div style="text-align: center;font-size:100%"><math>\underset{G}{min} \ \underset{D}{max}</math> <math>E_{x_1,x_2 ~ p_x|l(x_1)=l(x_2)}[log D(x_1,x_2)] + E_{v_1,v_2 ~ p_v,x~p_x}[log(1 − D(G(E(x),v_1),G(E(x),v_2)))]+E_{v∼p_v,x∼p_x}[log(1 − D(G(E(x), v), x))] </math></div><br />
<br />
<div style="text-align: center;font-size:100%">[[File:CGMV.png]]</div><br />
<br />
<br />
At inference time, as with the GMV model, we are interested in getting the encoder E and the<br />
generator G. These models may be used for generating new views of any object which is observed<br />
as an input sample x by computing its content vector E(x), then sampling <math>v ∼ p_v</math> and finally by<br />
computing the output G(E(x), v)<br />
<br />
==Experiments and Results==<br />
<br />
The authors have given an exhaustive set of results and experiments.<br />
<br />
Datasets: The two models were evaluated by performing experiments over four image datasets of various domains. Note that when supervision is available on the views (like CelebA for example where images are labeled with attributes) it is not used for learning models. The only supervision that is used is if two samples correspond to the same object or not.<br />
<br />
<div style="text-align: center;font-size:100%">[[File:table_data.png]]</div><br />
<br />
<br />
Model Architecture: Same architectures for every dataset. The images were rescaled to 3×64×64 tensors. The generator G and the discriminator D follow that of the DCGAN implementation proposed in Radford et al. (2015). The encoder E is similar to D with the only differences being the batch-normalization in the first layer and the last layer which doesn't have a non-linearity. The Adam optimizer was used, with a batch size of 128. The learning rates for G and D were set to 1*10<sup>-3</sup> and 2*10<sup>-4</sup> respectively for the GMV experiments. In the C-GMV experiments, learning rates of 5*10<sup>-5</sup> were used. Alternating gradient descent was used to optimize the different objectives of the network components (generator, encoder and discriminator).<br />
<br />
Baselines: Most existing methods are learned on datasets with view labeling. To fairly compare with alternative models, authors have built baselines working in the same conditions as the models in this paper. In addition, models are compared with the model from Mathieu et al. (2016). Results gained with two implementations are reported, the first one based on the implementation provided by the authors2 (denoted Mathieu et al. (2016)), and the second one (denoted Mathieu et al. (2016) (DCGAN) ) that implements the same model using architectures inspired from DCGAN Radford et al. (2015), which is more stable and that was tuned to allow a fair comparison with our approach. For pure multi-view generative setting, generative model(GMV) is compared with standard GANs that are learned to approximate the joint generation of multiple samples: DCGANx2 is learned to output pairs of views over the same object, DCGANx4 is trained on quadruplets, and DCGANx8 on eight different views. <br />
<br />
===Generating Multiple Contents and Views===<br />
<br />
Figure 1 shows examples of generated images by our model and Figure 4 shows images sampled by the DCGAN based models (DCGANx2, DCGANx4, and DCGANx8) on 3DChairs and CelebA datasets.<br />
<br />
<div style="text-align: center;font-size:100%">[[File:fig1_gmv.png]]</div><br />
<br />
<div style="text-align: center;font-size:100%">[[File:fig4_gmv.png]]</div><br />
<br />
<br />
Figure 5 shows additional results, using the same presentation, for the GMV model only on two other datasets. In the left hand block of Figure 5, each row shows different views generated given the same content. <br />
<br />
<div style="text-align: center;font-size:100%">[[File:fig5_gmv.png]]</div><br />
<br />
Figure 6 shows generated samples obtained by interpolation between two different view factors (left) or two content factors (right). Again, in the left and right hand block of Figure 6, each row shows different views generated given the same content. It allows us to have a better idea of the underlying view/content structure captured by GMV. We can see that our approach is able to smoothly move from one content/view to another content/view while keeping the other factor constant. This also illustrates that content and view factors are well independently handled by the generator i.e. changing the view<br />
does not modify the content and vice versa.<br />
<br />
<br />
<div style="text-align: center;font-size:100%">[[File:fig6_gmv.png]]</div><br />
<br />
===Generating Multiple Views of a Given Object===<br />
<br />
The second set of experiments evaluates the ability of C-GMV to capture a particular content from an input sample and to use this content to generate multiple views of the same object. Figure 7 and 8 illustrate the diversity of views in samples generated by our model and compare our results with those obtained with the CGAN model and to models from Mathieu et al. (2016). For each row, the input sample is shown in the left column. New views are generated from that input and shown to the right, with those generated from C_GMV in the centre, and those generated from CGAN on the far right.<br />
<br />
<div style="text-align: center;font-size:100%">[[File:fig7_gmv.png]]</div><br />
<br />
<br />
<div style="text-align: center;font-size:100%">[[File:fig8_gmv.png]]</div><br />
<br />
=== Evaluation of the Quality of Generated Samples ===<br />
<br />
There are usually several metrics to evaluate generative models. Some of them are: <br />
<ol><br />
<li>Inception Score</li><br />
<li>Latent Space Interpolation</li><br />
<li>log-likelihood (LL) score</li><br />
<li> minimum description length (MDL) score</li><br />
<li>minimum message length (MML) score</li><br />
<li>Akaike Information Criterion (AIC) score</li><br />
<li>Bayesian Information Criterion (BIC) score</li><br />
</ol><br />
<br />
<br />
<br />
<br />
<br />
The authors did sets of experiments aimed at evaluating the quality of the generated samples. They have been made on the CelebA dataset and evaluate (i) the ability of the models to preserve the identity of a person in multiple generated views, (ii) to generate realistic samples, (iii) to preserve the diversity in the generated views and (iv) to capture the view distributions of the original dataset.<br />
<br />
<div style="text-align: center;font-size:100%">[[File:tab3.png]]</div><br />
<br />
<br />
<div style="text-align: center;font-size:100%">[[File:tab4.png]]</div><br />
<br />
<br />
<div style="text-align: center;font-size:100%">[[File:table.png]]</div><br />
<br />
==Conclusion==<br />
<br />
The paper proposed a generative model, which can be learnt from multi-view data without any supervision. Moreover, it introduced a conditional version that allows generating new views of an input image. Using experiments, they proved that the model can capture content and view factors. Here, the paper showed that the application of architecture search to dense image prediction was achieved through a) The construction of a recursive search space leveraging innovation in the dense prediction literature b) construction of a fast proxy predictive of a large task. The learned architecture was shown to surpass human invented architectures across three dense image prediction tasks i.e scene parsing, person part segmentation and semantic segmentation. In the future, they are planning to use the method of this paper for data augmentation which can enrich training dataset. .<br />
<br />
==Future Work==<br />
The authors of the papers mentioned that they plan to explore using their model for data augmentation, as it can produce other data views for training, in both semi-supervised and one-shot/few-shot learning settings. <br />
<br />
==Critique==<br />
<br />
The main idea is to train the model with pairs of images with different views. It is not that clear as to what defines a view in particular. The algorithms are largely based on earlier concepts of GAN and CGAN The authors give reference to the previous papers tackling the same problem and clearly define that the novelty in this approach is not making use of view labels. The authors give a very thorough list of experiments which clearly establish the superiority of the proposed models to baselines.<br />
<br />
However, this paper only tested the model on rather constrained examples. As was observed in the results the proposed approach seems to have a high sample complexity relying on training samples covering the full range of variations for both specified and unspecified variations. Also, the proposed model does not attempt to disentangle variations within the specified and unspecified components.<br />
<br />
The method that the paper presented is novel and the paper is easy to follow. However, the authors only show a comparison between the proposed method and several baselines: DCGAN and CGAN and do not compare with the methods from Mathieu et al. 2016. In addition, the experiment result is empirical, we do not know the performance of this method in practice in the real word.<br />
<br />
==References==<br />
<br />
[1] Mickael Chen, Ludovic Denoyer, Thierry Artieres. MULTI-VIEW DATA GENERATION WITHOUT VIEW SUPERVISION. Published as a conference paper at ICLR 2018<br />
<br />
[2] Michael F Mathieu, Junbo Jake Zhao, Junbo Zhao, Aditya Ramesh, Pablo Sprechmann, and Yann LeCun. Disentangling factors of variation in deep representation using adversarial training. In Advances in Neural Information Processing Systems, pp. 5040–5048, 2016.<br />
<br />
[3] Mathieu Aubry, Daniel Maturana, Alexei Efros, Bryan Russell, and Josef Sivic. Seeing 3d chairs: exemplar part-based 2d-3d alignment using a large dataset of cad models. In CVPR, 2014.<br />
<br />
[4] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Advances in neural information processing systems, pp. 2672–2680, 2014.<br />
<br />
[5] Emily Denton and Vighnesh Birodkar. Unsupervised learning of disentangled representations from video. arXiv preprint arXiv:1705.10915, 2017.</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:table.png&diff=42340File:table.png2018-12-08T01:50:26Z<p>Aghabuss: Aghabuss uploaded a new version of File:table.png</p>
<hr />
<div></div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Reinforcement_Learning_of_Theorem_Proving&diff=42339Reinforcement Learning of Theorem Proving2018-12-08T01:46:50Z<p>Aghabuss: /* Experimental Results */</p>
<hr />
<div>== Introduction ==<br />
Automated reasoning over mathematical proof was a major motivation for the development of computer science. Automated theorem provers (ATP) can in principle be used to attack any formally stated mathematical problem and is a research area that has been present since the early 20th century [1]. As of today, state-of-art ATP systems rely on the fast implementation of complete proof calculi. such as resolution and tableau. However, they are still far weaker than trained mathematicians. Within current ATP systems, many heuristics are essential for their performance. As a result, <br />
in recent years machine learning has been used to replace such heuristics and improve the performance of ATPs.<br />
<br />
In this paper, the authors propose a reinforcement learning based ATP, rlCoP. The proposed ATP reasons within first-order logic. The underlying proof calculi are the connection calculi [2], and the reinforcement learning method is Monte Carlo tree search along with policy and value learning. It is shown that reinforcement learning results in a 42.1% performance increase compared to the base prover (without learning).<br />
<br />
== Related Work ==<br />
C. Kalizyk and J. Urban proposed a supervised learning based ATP, FEMaLeCoP, whose underlying proof calculi is the same as this paper in 2015 [3]. Their algorithm learns from existing proofs to choose the next tableau extension step. Since the MaLARea [8] system, number of iterations of a feedback loop between proving and learning have been explored, remarkably improving over human-designed heuristics when reasoning in large theories. However, such systems are known to only learn a high-level selection of relevant facts from a large knowledge base and delegate the internal proof search to standard ATP systems. S. Loos, et al. developed an supervised learning ATP system in 2017 [4], with superposition as their proof calculi. However, they chose deep neural network (CNNs and RNNs) as feature extractor. These systems are treated as black boxes in literature with not much understanding of their performances possible. <br />
<br />
In leanCoP [9], one of the simpler connection tableau systems, the next tableau extension step could be selected using supervised learning. In addition, the first experiments with Monte-Carlo guided proof search [5] have been done for connection tableau systems. The improvement over the baseline measured in that work is much less significant than here. This is closest to the authors' approach but the performance is poorer than this paper.<br />
<br />
On a different note, A. Alemi, et al. proposed a deep sequence model for premise selection in 2016 [6], and they claim to be the first team to involve deep neural networks in ATPs. Although premise selection is not directly linked to automated reasoning, it is still an important component in ATPs, and their paper provides some insights into how to process datasets of formally stated mathematical problems.<br />
<br />
== First Order Logic and Connection Calculi ==<br />
Here we assume basic first-order logic and theorem proving terminology, and we will offer a brief introduction of the bare prover and connection calculi. Let us try to prove the following first-order sentence.<br />
<br />
[[file:fof_sentence.png|frameless|450px|center]]<br />
<br />
This sentence can be transformed into a formula in Skolemized Disjunctive Normal Form (DNF), which is referred to as the "matrix".<br />
<br />
[[file:skolemized_dnf.png|frameless|450px|center]] <br />
[[file:matrix.png|frameless|center]] <br />
<br />
The original first-order sentence is valid if and only if the Skolemized DNF formula is a tautology. The connection calculi attempt to show that the Skolemized DNF formula is a tautology by constructing a tableau. We will start at the special node, root, which is an open leaf. At each step, we select a clause (for example, clause <math display="inline">P \wedge R</math> is selected in the first step), and add the literals as children for an existing open leaf. For every open leaf, examine the path from the root to this leaf. If two literals on this path are unifiable (for example, <math display="inline">Qx'</math> is unifiable with <math display="inline">\neg Qc</math>), this leaf is then closed. An example of a closed tableaux is shown in Figure 1. In standard terminology, it states that a connection is found on this branch.<br />
<br />
[[file:tableaux_example.png|thumb|center|Figure 1. An example of closed tableaux. Adapted from [2]]]<br />
<br />
The paper's goal is to close every leaf, i.e. on every branch, there exists a connection. If such state is reached, the paper has shown that the Skolemized DNF formula is a tautology, thus proving the original first-order sentence. As we can see from the constructed tableaux, the example sentence is indeed valid.<br />
<br />
In formal terms, the rules of connection calculi is shown in Figure 2, and the formal tableaux for the example sentence is shown in Figure 3. Each leaf is denoted as <math display="inline">subgoal, M, path</math> where <math display="inline">subgoal</math> is a list of literals that we need to find connection later, <math display="inline">M</math> stands for the matrix, and <math display="inline">path</math> stands for the path leading to this leaf.<br />
<br />
[[file:formal_calculi.png|thumb|600px|center|Figure 2. Formal connection calculi. Adapted from [2].]]<br />
[[file:formal_tableaux.png|thumb|600px|center|Figure 3. Formal tableaux constructed from the example sentence. Adapted from [2].]]<br />
<br />
To sum up, the bare prover follows a very simple algorithm. given a matrix, a non-negated clause is chosen as the first subgoal. The function ''prove(subgoal, M, path)'' is stated as follows:<br />
* If ''subgoal'' is empty<br />
** return ''TRUE''<br />
* If reduction is possible<br />
** Perform reduction, generating ''new_subgoal'', ''new_path''<br />
** return ''prove(new_subgoal, M, new_path)''<br />
* For all clauses in ''M''<br />
** If a clause can do extension with ''subgoal''<br />
** Perform extension, generating ''new_subgoal1'', ''new_path'', ''new_subgoal2''<br />
** return ''prove(new_subgoal1, M, new_path)'' and ''prove(new_subgoal2, M, path)''<br />
* return ''FALSE''<br />
<br />
It is important to note that the bare prover implemented in this paper is incomplete. Here is a pathological example. Suppose the following matrix (which is trivially a tautology) is feed into the bare prover. Let clause <math display="inline">P(0)</math> be the first subgoal. Clearly choosing <math display="inline">\neg P(0)</math> to extend will complete the proof.<br />
<br />
[[file:pathological.png|frameless|400px|center]] <br />
<br />
However, if we choose <math display="inline">\neg P(x) \lor P(s(x))</math> to do extension, the algorithm will generate an infinite branch <math display="inline">P(0), P(s(0)), P(s(s(0))) ...</math>. It is the task of reinforcement learning to guide the prover in such scenarios towards a successful proof.<br />
<br />
A technique called iterative deepening can be used to avoid such infinite loop, making the bare prover complete. Iterative deepening will force the prover to try all shorter proofs before moving into long ones, it is effective, but also waste valuable computing resource trying to enumerate all short proofs.<br />
<br />
In addition, the provability of first-order sentences is generally undecidable (this result is named the Church-Turing Thesis), which sheds light on the difficulty of automated theorem proving.<br />
<br />
== Mizar Math Library ==<br />
Mizar Math Library (MML) [7, 10] is a library of mathematical theories. The axioms behind the library is the Tarski-Grothendieck set theory, written in first-order logic. The library contains 57,000+ theorems and their proofs, along with many other lemmas, as well as unproven conjectures. Figure 4 shows a Mizar article of the theorem "If <math display="inline"> p </math> is prime, then <math display="inline"> \sqrt p </math> is irrational."<br />
<br />
[[file:mizar_article.png|thumb|center|Figure 4. An article from MML. Adapted from [6].]]<br />
<br />
The training and testing data for this paper is a subset of MML, the Mizar40, which is 32,524 theorems proved by automated theorem provers. Below is an example from the Mizar40 library, it states that with ''d3_xboole_0'' and ''t3_xboole_0'' as premises, we can prove ''t5_xboole_0''.<br />
<br />
[[file:mizar40_0.png|frameless|400px|center]]<br />
[[file:mizar40_1.png|frameless|600px|center]]<br />
[[file:mizar40_2.png|frameless|600px|center]]<br />
[[file:mizar40_3.png|frameless|600px|center]]<br />
<br />
== Monte Carlo Guidance ==<br />
<br />
Monte Carlo tree search (MCTS) is a heuristic search algorithm for some kinds of decision processes. The focus of Monte Carlo tree search is on the analysis of the most promising moves, expanding the search tree based on random sampling of the search space. Then the expansion will then be used to weight the node in the search tree.<br />
<br />
In the reinforcement learning setting, the action is defined as one inference (either reduction or extension). The proof state is defined as the whole tableaux. To implement Monte-Carlo tree search, each proof state <math display="inline"> i </math> needs to maintain three parameters, its prior probability <math display="inline"> p_i </math>, its total reward <math display="inline"> w_i </math>, and number of its visits <math display="inline"> n_i </math>. If no policy learning is used, the prior probabilities are all equal to one. <br />
<br />
A simple heuristic is used to estimate the future reward of leaf states: suppose leaf state <math display="inline"> i </math> has <math display="inline"> G_i </math> open subgoals, the reward is computed as <math display="inline"> 0.95 ^ {G_i} </math>. This will be replaced once value learning is implemented.<br />
<br />
The standard UCT formula is chosen to select the next actions in the playouts<br />
\begin{align}<br />
{\frac{w_i}{n_i}} + 2 \cdot p_i \cdot {\sqrt{\frac{\log N}{n_i}}}<br />
\end{align}<br />
where <math display="inline"> N </math> stands for the total number of visits of the parent node.<br />
<br />
The bare prover is asked to play <math display="inline"> b </math> playouts of length <math display="inline"> d </math> from the empty tableaux, each playout backpropagates the values of proof states it visits. After these <math display="inline"> b </math> playouts a special action (inference) is made, corresponding to an actual move, resulting in a new bigstep tableaux. The next <math display="inline"> b </math> playouts will start from this tableaux, followed by another bigstep, etc.<br />
<br />
== Policy Learning and Guidance ==<br />
<br />
From many runs of MCT, we will know the optimal prior probability of actions (inferences) in particular proof states, we can extract the frequency of each action <math display="inline"> a </math>, and normalize it by dividing with the average action frequency at that state, resulting in a relative proportion <math display="inline"> r_a \in (0, \infty) </math>. We characterize the proof states for policy learning by extracting human-engineered features. Also, we characterize actions by extracting features from the clause chosen and literal chosen as well. Thus we will have a feature vector <math display="inline"> (f_s, f_a) </math>. <br />
<br />
The feature vector <math display="inline"> (f_s, f_a) </math> is regressed against the associated <math display="inline"> r_a </math>.<br />
<br />
During the proof search, the prior probabilities <math display="inline"> p_i </math> of available actions <math display="inline"> a_i </math> in a state <math display="inline"> s </math> is computed as the softmax of their predictions.<br />
<br />
Training examples are only extracted from big step states, making the amount of training data manageable.<br />
<br />
== Value Learning and Guidance ==<br />
<br />
Bigstep states are also used for proof state evaluation. For a proof state <math display="inline"> s </math>, if it corresponds to a successful proof, the value is assigned as <math display="inline"> v_s = 1 </math>. If it corresponds to a failed proof, the value is assigned as <math display="inline"> v_s = 0 </math>. For other scenarios, denote the distance between state <math display="inline"> s </math> and a successful state as <math display="inline"> d_s </math>, then the value is assigned as <math display="inline"> v_s = 0.99^{d_s} </math> <br />
<br />
Proof state feature <math display="inline"> f_s </math> is regressed against the value <math display="inline"> v_s </math>. During the proof search, the reward of leaf states are computed from this prediction.<br />
<br />
== Features and Learners ==<br />
For proof states, features are collected from the whole tableaux (subgoals, matrix, and paths). Each unique symbol is represented by an integer, and the tableaux can be represented as a sequence of integers. Term walk is implemented to combine a sequence of integers into a single integer by multiplying components by a fixed large prime and adding them up. Then the resulting integer is reduced to a smaller feature space by taking modulo by a large prime.<br />
<br />
For actions the feature extraction process is similar, but the term walk is over the chosen literal and the chosen clause.<br />
<br />
In addition to the term walks, they also added several common features: number of goals, total symbol size of all goals, length of active paths, number of current variable instantiations, most common symbols.<br />
<br />
The whole project is implemented in OCaml, and XGBoost is ported into OCaml as the learner.<br />
<br />
== Experimental Results ==<br />
In the paper, the dataset they were using is Mizar40. They divided the mizar40 dataset into training and testing set, with a ratio of 9 to 1. According to the author, the split is a random split. During the experiment, the authors' method was able to prove 32524 statements out of 146700 statements. The authors' main approach is transforming the data from First-order logic form into DNF( disjunctive normal form), <br />
The authors use the M2k dataset to compare the performance of mlCoP, the bare prover and rlCoP using only UCT. There were 577 test problems that the rlCop trained. <br />
*Performance without Learning<br />
Table 3 shows the baseline result. The Performance of the bare prover is significantly lower than mlCoP and rlCoP without policy/value.<br />
[[file:table3.png|550px|center]]<br />
*Reinforcement Learning of Policy Only<br />
In this experiment, the authors evaluated on the dataset rlCoP with UCT using policy learning only. They used the policy training data from previous iterations to train a new predictor after each iteration. Which means only the first iteration ran without policy while all the rest iterations used previous policy training data.<br />
From Table 4, rlCoP is better than mlCoP run with the much higher <math>4 ∗ 10^{6}</math> inference limit after fourth iteration. <br />
[[file:table4.png|550px|center]]<br />
*Reinforcement Learning of Value Only<br />
This experiment was similar to the last one, however, they used only values rather than learned policy. From Table 5, the performance of rlCoP is close to mlCoP but below it after 20 iterations, and it is far below rlCoP using only policy learning.<br />
[[file:table5.png|550px|center]]<br />
*Reinforcement Learning of Policy and Value<br />
From Table 6, the performance of rlCoP is 19.4% more than mlCoP with <math>4 ∗ 10^{6}</math> inferences, 13.6% more than the best iteration of rlCoP with policy only, and 44.3% more than the best iteration of rlCoP with value only after 20 iterations.<br />
[[file:table6.png|550px|center]]<br />
Besides, they also evaluated the effect of the joint reinforcement learning of both policy and value. Replacing final policy and value with the best one from policy-only or value-only both decreased performance.<br />
<br />
*Evaluation on the Whole Miz40 Dataset.<br />
The authors split Mizar40 dataset into 90% training examples and 10% testing examples. 200,000 inferences are allowed for each problem. 10 iterations of policy and value learning are performed (based on MCT). The training and testing results are shown as follows. In the table, ''mlCoP'' represents for the bare prover with iterative deepening (i.e. a complete automated theorem prover with connection calculi), and ''bare prover'' stands for the prover implemented in this paper, without MCT guidance.<br />
<br />
[[file:atp_result0.jpg|frane|550px|center|Figure 5a. Experimental result on Mizar40 dataset]]<br />
[[file:atp_result1.jpg|frame|550px|center|Figure 5b. More experimental result on Mizar40 dataset]]<br />
<br />
As shown by these results, reinforcement learning leads to a significant performance increase for automated theorem proving, the 42.1% performance improvement is unusually high, since the published improvement in this field is typically between 3% and 10%. [1]<br />
<br />
Besides these results, there were also found that some test problems could be solved with rlCoP easily but mlCoP could not.<br />
<br />
[[file:picture3.png|frame|550px|center|Figure 6: The MCTS tree for the WAYBEL 0:28 problem at the moment when the proof is found. For each node we display the predicted probabilityp, the number of visitsnand the average rewardr=w/n. For the (thicker) nodes leading to the proof the corresponding local proof goals arepresented on the right.]]<br />
<br />
== Conclusions ==<br />
In this work, the authors developed an automated theorem prover that uses no domain engineering and instead replies on MCT guided by reinforcement learning. The resulting system is more than 40% stronger than the baseline system. The authors believe that this is a landmark in the field of automated reasoning, demonstrating that building general problem solvers by reinforcement learning is a viable approach. [1]<br />
<br />
The authors pose that some future research could include strong learning algorithms to characterize mathematical data. The development of suitable deep learning architectures will help the algorithm characterize semantic and syntactic features of mathematical objects which will be crucial to create strong assistants for mathematics and hard sciences.<br />
<br />
== Critiques ==<br />
Until now, automated reasoning is relatively new to the field of machine learning, and this paper shows a lot of promise in this research area.<br />
<br />
The feature extraction part of this paper is less than optimal. It is my opinion that with proper neural network architecture, deep learning extracted features will be superior to human-engineered features, which is also shown in [4, 6].<br />
<br />
Also, the policy-value learning iteration is quite inefficient. The learning loop is:<br />
* Loop <br />
** Run MCT with the previous model on an entire dataset<br />
** Collect MCT data<br />
** Train a new model<br />
If we adopt this to an online learning scheme by learning as soon as MCT generates new data, and update the model immediately, there might be some performance increase.<br />
<br />
The experimental design of this paper has some flaws. The authors compare the performance of ''mlCoP'' and ''rlCoP'' by limiting them to the same number of inference steps. However, every inference step of ''rlCoP'' requires additional machine learning prediction, which costs more time. A better way to compare their performance is to set a time limit.<br />
<br />
It would also be interesting to study automated theorem proving in another logic system, like high order logic, because many mathematical concepts can only be expressed in higher-order logic.<br />
<br />
== References ==<br />
[1] C. Kaliszyk, et al. Reinforcement Learning of Theorem Proving. NIPS 2018.<br />
<br />
[2] J. Otten and W. Bibel. leanCoP: Lean Connection-Based Theorem Proving. Journal of Symbolic Computation, vol. 36, pp. 139-161, 2003.<br />
<br />
[3] C. Kaliszyk and J. Urban. FEMaLeCoP: Fairly Efficient Machine Learning Connection Prover. Lecture Notes in Computer Science. vol. 9450. pp. 88-96, 2015.<br />
<br />
[4] S. Loos, et al. Deep Network Guided Proof Search. LPAR-21, 2017.<br />
<br />
[5] M. F¨arber, C. Kaliszyk, and J. Urban. Monte Carlo tableau proof search. In L. de Moura, editor,<br />
26th International Conference on Automated Deduction (CADE), volume 10395 of LNCS,<br />
pages 563–579. Springer, 2017.<br />
<br />
[6] A. Alemi, et al. DeepMath-Deep Sequence Models for Premise Selection. NIPS 2016.<br />
<br />
[7] Mizar Math Library. http://mizar.org/library/<br />
<br />
[8] J. Urban, G. Sutcliffe, P. Pudla ́k, and J. Vyskocˇil. MaLARea SG1 - Machine Learner for Automated Reasoning with Semantic Guidance. In A. Armando, P. Baumgartner, and G. Dowek, editors, IJCAR, volume 5195 of LNCS, pages 441–456. Springer, 2008.<br />
<br />
[9] J. Otten and W. Bibel. leanCoP: lean connection-based theorem proving. J. Symb. Comput., 36(1-2):139–161, 2003.<br />
<br />
[10] A. Grabowski, A. Korniłowicz, and A. Naumowicz. Mizar in a nutshell. J. Formalized Rea-<br />
soning, 3(2):153–245, 2010</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:picture3.png&diff=42338File:picture3.png2018-12-08T01:46:25Z<p>Aghabuss: </p>
<hr />
<div></div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Predicting_Floor_Level_For_911_Calls_with_Neural_Network_and_Smartphone_Sensor_Data&diff=42337Predicting Floor Level For 911 Calls with Neural Network and Smartphone Sensor Data2018-12-08T01:42:45Z<p>Aghabuss: </p>
<hr />
<div><br />
<br />
=Introduction=<br />
<br />
In highly populated cities with many buildings, locating individuals in the case of an emergency is an important task. For emergency responders, time is of essence. Therefore, accurately locating a 911 caller plays an integral role in this important process.<br />
<br />
The motivation for this problem is in the context of 911 calls: victims trapped in a tall building who seek immediate medical attention, locating emergency personnel such as firefighters or paramedics, or a minor calling on behalf of an incapacitated adult. <br />
<br />
In this paper, a novel approach is presented to accurately predict floor level for 911 calls by leveraging neural networks and sensor data from smartphones.<br />
<br />
In large cities with tall buildings, relying on GPS or Wi-Fi signals does not always lead to an accurate location of a caller.<br />
<br />
<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">[[File:17floor.png|250px]]<br />
[[File:19floor.png|250px]]</div><br />
<br />
<br />
In this work, there are two major contributions. The first is that they trained a LSTM to classify whether a smartphone was either inside or outside a building using GPS, Received signal strength indication (RSSI), and magnetometer sensor readings. The model is compared with baseline models like feed-forward neural networks, logistic regression, SVM, HMM, and Random Forests. The second contribution is an algorithm, which uses the output of the trained LSTM, to predict change in the barometric pressure of the smartphone from when it first entered the building against that of its current location within the building. In the final part of their algorithm, they are able to predict the floor level by clustering the measurements of height.<br />
<br />
The model does not rely on the external sensors placed inside the building, prior knowledge of the building, nor user movement behaviour. The only input it looks at is the GPS and the barometric signal from the phone. Finally, they also talk about the application of this algorithm in a variety of other real-world situations. <br />
<br />
All the codes and data related to this article are available here[[https://github.com/williamFalcon/Predicting-floor-level-for-911-Calls-with-Neural-Networks-and-Smartphone-Sensor-Data]]<br />
<br />
=Related Work=<br />
<br />
<br />
In general, previous work falls under two categories. The first category of methods is the classification methods based on the user's activity. <br />
Therefore, some current methods leverage the user's activity to predict which is based on the offset in their movement [2]. These activities include running, walking, and moving through the elevator.<br />
The second set of methods focus more on the use of a barometer which measures the atmospheric pressure. As a result, utilizing a barometer can provide the changes in altitude.<br />
<br />
Avinash Parnandi and his coauthors used multiple classifiers in the predicting the floor level [2]. The steps in their algorithmic process are: <br />
<ol><br />
<li> Classifier to predict whether the user is indoors or outdoors</li><br />
<li> Classifier to identify if the activity of the user, i.e. walking, standing still etc. </li><br />
<li> Classifier to measure the displacement</li><br />
</ol><br />
<br />
One of the downsides of this work is to achieve the high accuracy that the user's step size is needed, therefore heavily relying on pre-training to the specific users. In a real world application of this method, this would not be practical.<br />
<br />
<br />
Song and his colleagues model the way or cause of ascent. That is, was the ascent a result of taking the elevator, stairs or escalator [3]. Then by using infrastructure support of the buildings and as well as additional tuning they are able to predict floor level. <br />
This method also suffers from relying on data specific to the building. <br />
<br />
Overall, these methods suffer from relying on pre-training to a specific user, needing additional infrastructure support, or data specific to the building. The method proposed in this paper aims to predict floor level without these constraints.<br />
<br />
=Method=<br />
<br />
<br />
In their paper, the authors claim that to their knowledge "there does not exist a dataset for predicting floor heights" [4].<br />
<br />
To collect data, the authors developed an iOS application (called Sensory) that runs on an iPhone 6s to aggregate the data. They used the smartphone's sensors to record different features such as barometric pressure, GPS course, GPS speed, RSSI strength, GPS longitude, GPS latitude, and altitude. The app streamed data at 1 sample per second, and each datum contained the different sensor measurements mentions earlier along with environment contexts like building floors, environment activity, city name, country name, and magnetic strength.<br />
<br />
The data collection procedure for '''indoor-outdoor classifier''' was described as follows:<br />
1) Start outside a building. 2) Turn Sensory on, set indoors to 0. 3) Start recording. 4) Walk into and out of buildings over the next n seconds. 5) As soon as we enter the building (cross the outermost door) set indoors to 1. 6) As soon as we exit, set indoors to 0. 7) Stop recording. 8) Save data as CSV for analysis. This procedure can start either outside or inside a building without loss of generality.<br />
<br />
The following procedure generates data used to '''predict a floor change''' from the entrance floor to the end floor:<br />
1) Start outside a building. 2) Turn Sensory on, set indoors to 0. 3) Start recording. 4) Walk into and out of buildings over the next n seconds. 5) As soon as we enter the building (cross the outermost door) set indoors to 1. 6) Finally, enter a building and ascend/descend to any story. 7) Ascend through any method desired, stairs, elevator, escalator, etc. 8) Once at the floor, stop recording. 9) Save data as CSV for analysis.<br />
<br />
Their algorithm was used to predict floor level which is a 3 part process:<br />
<br />
<ol><br />
<li> Classifying whether smartphone is indoor or outdoor </li><br />
<li> Indoor/Outdoor Transition detector</li><br />
<li> Estimating vertical height and resolving to absolute floor level </li><br />
</ol><br />
<br />
==1) Classifying Indoor/Outdoor ==<br />
<br />
<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">[[File:classifierfloor.png|800px]] </div><br />
<br />
From [5] they are using 6 features which were found through forests of trees feature reduction. The features are smartphone's barometric pressure (<math>P</math>), GPS vertical accuracy (<math>GV</math>), GPS horizontal accuracy (<math>GH</math>), GPS speed (<math>S</math>), device RSSI level (<math>rssi</math>), and magnetometer total reading (<math>M</math>).<br />
<br />
The magnetometer total reading was calculated from given the 3-dimensional reading <math>x, y, z </math><br />
<br />
<br />
<div style="text-align: center;">Total Magnetic field strength <math>= M = \sqrt{x^{2} + y^{2} + z^{2}}</math></div><br />
<br />
They used a 3 layer LSTM where the inputs are <math> d </math> consecutive time steps. The output <math> y = 1 </math> if smartphone is indoor and <math> y = 0 </math> if smartphone is outdoor.<br />
<br />
In their design they set <math> d = 3</math> by random search [6]. The point to make is that they wanted the network to learn the relationship given a little bit of information from both the past and future.<br />
<br />
For the overall signal sequence: <math> \{x_1, x_2,x_j, ... , x_n\}</math> the aim is to classify <math> d </math> consecutive sensor readings <math> X_i = \{x_1, x_2, ..., x_d \} </math> as <math> y = 1 </math> or <math> y = 0 </math> as noted above.<br />
<br />
<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">[[File:Table5.png|750px]] </div><br />
<br />
This is a critical part of their system and they only focus on the predictions in the subspace of being indoors. <br />
<br />
They have trained the LSTM to minimize the binary cross entropy between the true indoor state <math> y </math> of example <math> i </math>. <br />
<br />
The cost function is shown below:<br />
<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">[[File:costfunction.png|450px]] </div><br />
<br />
The final output of the LSTM is a time-series <math> T = {t_1, t_2, ..., t_i, t_n} </math> where each <math> t_i = 0, t_i = 1 </math> if the point is outside or inside respectively.<br />
<br />
==2) Transition Detector ==<br />
<br />
Given the predictions from the previous step, now the next part is to find when the transition of going in or out of a building has occurred.<br />
<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">[[File:transition.png|400px]] </div><br />
In this figure, they convolve filters <math> V_1, V_2</math> across the predictions T and they pick a subset <math>s_i </math> such that the Jacard distance (defined below) is <math> >= 0.4 </math><br />
<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">[[File:v1v2.png|250px]] </div><br />
Jacard Distance:<br />
<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">[[File:jacard.png|450px]]</div><br />
<br />
After this process, we are now left with a set of <math> b_i</math>'s describing the index of each indoor/outdoor transition. The process is shown in the first figure.<br />
<br />
==3) Vertical height and floor level ==<br />
<br />
<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">[[File:resolvefloor.png|700px]] </div><br />
<br />
[3] suggested the use of a reference barometer or beacons as a way to determine the entrances to a building.<br />
<br />
However, such need is eliminated by the authors' approach. The authors' second key contribution is to use the LSTM IO predictions to help identifying these indoor transitions into the building. The LSTM provides a self-contained estimator of a building’s entrance without relying on external sensor information on a user’s body or beacons placed inside a building’s lobby. [4]<br />
<br />
In the final part of the system, the vertical offset needs to be computed given the smartphone's last known location i.e. the last known transition which can easily be computed given the set of transitions from the previous step. All that needs to be done is to pull the index of most recent transition from the previous step and set <math> p_0</math> to the lowest pressure within a ~ 15-second window around that index.<br />
<br />
The second parameter is <math> p_1 </math> which is the current pressure reading. In order to generate the relative change in height <math> m_\Delta</math><br />
<br />
After plugging this into the formula defined above we are now left with a scalar value which represents the height displacement between the entrance and the smartphone's current location of the building [7].<br />
<br />
In order to resolve to an absolute floor level, they use the index number of the clusters of <math> m_\Delta</math> 's. As seen above <math> 5.1 </math> is the third cluster implying floor number 3.<br />
<br />
=Experiments and Results=<br />
<br />
==Dataset==<br />
<br />
In this paper, an iOS app called Sensory is developed which is used to collect data on an iPhone 6. The following sensor readings were recorded: '''indoors''', '''created at''', '''session id''', '''floor''', '''RSSI strength''', '''GPS latitude''', '''GPS longitude''', '''GPS vertical accuracy''', '''GPS horizontal accuracy''', '''GPS course''', '''GPS speed''', '''barometric relative altitude''', '''barometric pressure''', '''environment context''', '''environment mean building floors''', '''environment activity''', '''city name''', '''country name''', '''magnet x''', '''magnet y''', '''magnet z''', '''magnet total'''.<br />
<br />
As soon as the user enters or exits a building, the indoor-outdoor data has to be manually entered. To gather the data for the floor level prediction, the authors conducted 63 trials among five different buildings throughout New York City. Since unsupervised learning was being used, the actual floor level was recorded manually for the validation purposes only.<br />
<br />
<br />
<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">[[File:ioaccuracy.png|450px]] </div><br />
<br />
All of these classifiers were trained and validated on data from a total of 5082 data points. The set split was 80% training and 20% validation. <br />
For the LSTM the network was trained for a total of 24 epochs with a batch size of 128 and using an Adam optimizer where the learning rate was 0.006. <br />
Although the baselines performed considerably well the objective here was to show that an LSTM can be used in the future to model the entire system with an LSTM.<br />
<br />
<br />
<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">[[File:flooraccuracy.png|650px]] </div><br />
<br />
The above chart shows the success that their system is able to achieve in the floor level prediction.<br />
<br />
The performance was measured in terms of how many floors were travelled rather than the absolute floor number. Because different buildings might have their floors differently numbered. They used different m values in 2 tests. One applies the same m value across all building and the other one applied specific m values on different buildings. The result showed that this specification on m values hugely increased the accuracy.<br />
<br />
=Future Work=<br />
The first part of the system used an LSTM for indoor/outdoor classification. Therefore, this separate module can be used in many other location problems. Working on this separate problem seems to be an approach that the authors will take. They also would like to aim towards modeling the whole problem within the LSTM in order to generate the floor level predictions solely from sensor reading data.<br />
<br />
=Critique=<br />
<br />
In this paper, the authors presented a novel system which can predict a smartphone's floor level with 100% accuracy, which has not been done. Previous work relied heavily on pre-training and information regarding the building or users beforehand. Their work can generalize well to many types of tall buildings which are more than 19 stories. Another benefit to their system is that they don't need any additional infrastructure support in advance making it a practical solution for deployment. <br />
<br />
With rising number of smartphone users, cellular network capacity is reaching its limits and not able to cater to multiple users. One of the major concerns being the indoor cellular coverage and seamless mobility between indoor-outdoor cellular networks. The proposed solution can enable in providing this connectivity between cells for example handover between an indoor pico/nano cell, Wi-Fi network to an outdoor macro cell network; moreover with the floor detection algorithm, connectivity can be improved for users under low coverage areas such as basements, underground car parking, etc. Hence this can be integrated into one of the 5G use cases for improved network coverage. <br />
<br />
A weakness is that they claim they can get 100% accuracy, but this is only if they know the floor to ceiling height, and their accuracy relies on this key piece of information. Otherwise, when conditioned on the height of the building their accuracy drops by 35% to 65%. Also, the article's ideas are sometimes out of order and are repeated in cycles.<br />
<br />
It is also not clear that the LSTM is the best approach especially since a simple feedforward network achieved the same accuracy in their experiments.<br />
<br />
They also go against their claim stated at the beginning of the paper where they say they "..does not require the use of beacons, prior knowledge of the building infrastructure..." as in their clustering step they are in a way using prior knowledge from previous visits [4].<br />
<br />
The authors also recognize several potential failings of their method. One is that their algorithm will not differentiate based on the floor of the building the user entered on (if there are entrances on multiple floors). In addition, they state that a user on the roof could be detected as being on the ground floor. It was not mentioned/explored in the paper, but a person being on a balcony (ex: attached to an apartment) may have the same effect. These sources of error will need to be corrected before this or a similar algorithm is implemented; otherwise, the algorithm may provide the misleading data to rescue crews, etc.<br />
<br />
Overall this paper is not too novel, as they don't provide any algorithmic improvement over the state of the art. Their methods are fairly standard ML techniques and they have only used out of the box solutions. There is no clear intuition why the proposed work well for the authors. This application could be solved using simpler methods like having an emergency push button on each floor. Moreover, authors don't provide sufficient motivation for why deep learning would be a good solution to this problem.<br />
<br />
The proposed model could introduce privacy risks such as illegal surveillance of mobile phone user and private facilities.<br />
<br />
=Potential Pitfall of the System=<br />
<br />
One of the main criticisms for barometric pressure based systems is the unpredictability of barometric pressure as a sensor measurement due to external factors and changing weather conditions.<br />
<br />
=References=<br />
<br />
[1] Sepp Hochreiter and Jurgen Schmidhuber. Long short-term memory. Neural Computation, 9(8):<br />
1735–1780, 1997.<br />
<br />
[2] Parnandi, A., Le, K., Vaghela, P., Kolli, A., Dantu, K., Poduri, S., & Sukhatme, G. S. (2009, October). Coarse in-building localization with smartphones. In International Conference on Mobile Computing, Applications, and Services (pp. 343-354). Springer, Berlin, Heidelberg.<br />
<br />
[3] Wonsang Song, Jae Woo Lee, Byung Suk Lee, Henning Schulzrinne. "Finding 9-1-1 Callers in Tall Buildings". IEEE WoWMoM '14. Sydney, Australia, June 2014.<br />
<br />
[4] W Falcon, H Schulzrinne, Predicting Floor-Level for 911 Calls with Neural Networks and Smartphone Sensor Data, 2018<br />
<br />
[5] Kawakubo, Hideko and Hiroaki Yoshida. “Rapid Feature Selection Based on Random Forests for High-Dimensional Data.” (2012).<br />
<br />
[6] James Bergstra and Yoshua Bengio. 2012. Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13 (February 2012), 281-305.<br />
<br />
[7] Greg Milette, Adam Stroud: Professional Android Sensor Programming, 2012, Wiley India</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Searching_For_Efficient_Multi_Scale_Architectures_For_Dense_Image_Prediction&diff=42336Searching For Efficient Multi Scale Architectures For Dense Image Prediction2018-12-08T01:40:17Z<p>Aghabuss: /* What they used in this paper */</p>
<hr />
<div><br />
[Need add more pics and references]<br />
=Introduction=<br />
<br />
The design of neural network architectures is an important component for the success of machine learning and data science projects. In recent years, the field of Neural Architecture Search (NAS) has emerged, which is to automatically find an optimal neural architecture for a given task in a well-defined architecture space. The resulting architectures have often outperform networks designed by human experts on tasks such as image classification and natural language processing. [2,3,4] <br />
<br />
This paper presents a meta-learning technique to have computers search for a neural architecture that performs well on the task of dense image segmentation, mainly focused on the problem of scene labeling.<br />
<br />
=Motivation=<br />
<br />
The part of deep neural networks(DNN) success is largely due to the fact that it greatly reduces the work in feature engineering. This is because DNNs have the ability to extract useful features given the raw input. However, this creates a new paradigm to look at - network engineering. In order to extract significant features, an appropriate network architecture must be used. Hence, the engineering work is shifted from feature engineering to network architecture design for better abstraction of features.<br />
<br />
The motivation for NAS is to establish a guiding theory behind how to design the optimal network architecture. Given that there is an <br />
abundant amount of computational resources available, an intuitive solution is to define a finite search space for a computer to search for optimal network structures and hyperparameters.<br />
<br />
=Related Work =<br />
<br />
This paper focuses on two main literature research topics. One is the neural architecture search (NAS) and the other is the Multi-Scale representation for dense image prediction. Neural architecture search trains a controller network to generate neural architectures. The following are the important research directions in this area: <br />
<br />
1) One kind of research transfers architectures learned on a proxy dataset to more challenging datasets and demonstrates superior performance over many human-invented architectures.<br />
<br />
2) Reinforcement learning, evolutionary algorithms and sequential model-based optimization have been used to learn network structures. <br />
<br />
3) Some other works focus on increasing model size, sharing model weights to accelerate model search or a continuous relaxation of the architecture representation. <br />
<br />
4) Some recent methods focus on proposing methods for embedding an exponentially large number of architectures in a grid arrangement for semantic segmentation tasks. <br />
<br />
In the area of multi-scale representation for dense image prediction the following are useful prior work: <br />
<br />
1) State of the art methods use Convolutional Neural Nets. There are different methods proposed for supplying global features and context information to perform pixel level classification. <br />
<br />
2) Some approaches focus on how to efficiently encode multi-scale context information in a network architecture like designing models that take an input an image pyramid so that large-scale objects are captured by the downsampled image. <br />
<br />
3) Research also tried to come up with a theme on how best to tune the architecture to extract context information. Some works focus on sampling rates in atrous convolution to encode multi-scale context. Some others build context module by gradually increasing the rate on top of belief maps.<br />
<br />
=NAS Overview=<br />
<br />
NAS essentially turns a design problem into a search problem. As a search problem in general, we need a clear definition of three things:<br />
<ol><br />
<li> Search space</li><br />
<li> Search strategy</li><br />
<li> Performance Estimation Strategy</li><br />
</ol><br />
The search space is easy to understand, for instance defining a hyperparameter space to consider for our optimal solution. In the field of NAS, the search space is heavily dependent on the assumptions we make on the neural architecture. The search strategy details how to explore the search space. The evaluation strategy refers to taking an input of a set of hyperparameters, and from there evaluating how well our model fits. In the field of NAS, it is typical to find architectures that achieve high predictive performance on unseen data. [5]<br />
<br />
We will take a deep dive into the above three dimensions of NAS in the following sections<br />
<br />
=Search Space=<br />
The purpose of architecture search space is to design a space that can express various state-of-the-art architectures, and able to identify good models.<br />
<br />
There are typically three ways of defining the search space.<br />
==Chain-structured neural networks ==<br />
<br />
<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">[[File:Screen_Shot_2018-11-10_at_6.03.00_PM.png|150px]]<br />
</div><br />
[5]<br />
The chain structed network can be viewd as sequence of n layers, where the layer <math> i</math> recives input from <math> i-1</math> layer and the output serves<br />
the input to layer <math> i+1</math>.<br />
<br />
The search space is then parametrized by:<br />
1) Number of layers n<br />
2) Type of operations can be executed on each layer<br />
3) Hyperparameters associated with each layer<br />
<br />
==Multi-branch networks ==<br />
<br />
<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">[[File:Screen Shot 2018-11-10 at 6.03.08 PM.png|400px]]</div><br />
<br />
[5]<br />
This architecture allows significantly more degrees of freedom. It allows shortcuts and parallel branches. Some of the ideas are inspired by human hand-crafted networks. For example, the shortcut from shallow layers directly to the deep layers are coming from networks like ResNet [6]<br />
<br />
The search space includes the search space of chain-structured networks, with additional freedom of adding shortcut connections and allowing parallel branches to exist.<br />
<br />
==Cell/Block ==<br />
<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">[[File:Screen Shot 2018-11-10 at 6.03.31 PM.png|600px]]</div><br />
<br />
[6]<br />
This architecture defines a cell which is used as the building block of the neural network. A good analogy here is to think a cell as a lego piece, and you can define different types of cells as different<br />
lego pieces. And then you can combine them together to form a new neural structure. <br />
<br />
The search space includes the internal structure of the cell and how to combine these blocks to form the resulting architecture.<br />
<br />
==What they used in this paper ==<br />
<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">[[File:Screen Shot 2018-11-10 at 6.50.04 PM.png|500px]]<br />
</div><br />
[1]<br />
This paper's approach is very close to the Cell/Block approach above<br />
<br />
The paper defines two components: The "network backbone" and a cell unit called "DPC" which represented by a directed acyclic graph (DAG) with five branches (i.e. the optimal value, which gives a good balance between flexibility and computational tractability). A DAG is a finite directed graph with no directed cycles which consists of finitely many vertices and edges, with each edge directed from one vertex to another, such that there is no way to start at any vertex <math>v</math> and follow a consistently-directed sequence of edges that eventually loops back to <math>v</math> again. The network backbone's job is to take input image as a tensor and return a feature map f that is a supposedly good abstraction of the image. The DPC is what they introduced in this paper, short for Dense Prediction Cell, that is a recursive search space to encode multi-scale context information for dense prediction tasks. In theory, the search space consists of what they choose for the network backbone and the internal structure of the DPC. In practice, they just used MobileNet and Modified Xception net as the backbone. So the search space only consists of the internal structure of the DPC cell.<br />
<br />
For the network backbone, they simply choose from existing mature architecture. They used networks like Mobile-Net-v2, Inception-Net, and e.t.c. For the structure of DPC, they define a smaller unit of called branch. A branch is a triple of (Xi, OP, Yi), where Xi is an input tensor, and OP is the operation that can be done on the tensor, and Yi is the resulting after the Operation. <br />
<br />
In the paper, they set each DPC consists of 5 cells for the balance expressivity and computational tractability.<br />
<br />
The operator space, OP, is defined as the following set of functions:<br />
<ol><br />
<li>Convolution with a 1 × 1 kernel.</li><br />
<li>3×3 atrous separable convolution with rate rh×rw, where rh and rw ∈ {1, 3, 6, 9, . . . , 21}. </li><br />
<li>Average spatial pyramid pooling with grid size gh × gw, where gh and gw ∈ {1, 2, 4, 8}. </li><br />
</ol><br />
<br />
For the spatial pyramid pooling operation, average pooling is performed in each grid. After the<br />
average pooling, a 1×1 convolution is applied and the then the resize back the features to have the same spatial resolution as the input tensor.<br />
<br />
Separable convolution with 256 filters is employed for all convolutions and 3x3 atrous convolutions with sampling rates rh x rw allows for capturing object scales with different aspect ratios. This is illustrated in the diagram below: <br />
<br />
[[File:NAS_fig2.png|center|500px]]<br />
<br />
Average spatial pyramid pooling is performs mean pooling on the last convolution layer (either convolution or sub sampling) and produces a N*B dimensional vector (where N=Number of filters in the convolution layer, B= Number of Bins). The vector is in turn fed to the fully connected layer. The number of bins is a constant value. Therefore, the vector dimension remains constant irrespective of the input image size.<br />
<br />
The resulting search space is able to encode all the main state-of-the-art architectures(i.e. Deformable Convnets [11], ASPP, Dense-ASPP [12] etc.), but these encoded architectures are more diverse since each branch of a DPC cell could build contextual information through parallel or cascaded representations. The number of potential architectures may determine the potential diversity of the search space. For <math display="inline">i</math>-th branch, there are <math display="inline">i</math> possible inputs, including the last feature maps produced by the network backbone, all the outputs from previous branch (<math display="inline">i.e., Y_1,...,Y_{i-1}</math>), and also 1 + 8×8 + 4×4 = 81 functions in the operator space, resulting in <math display="inline">i × 81</math> possible options. Therefore, for B = 5, the search space size is B! × 81^B ≈ 4.2 × 10^11 configurations.<br />
<br />
[[File:picture2.png|center|500px]]<br />
<br />
=Search Strategy=<br />
<br />
<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">[[File:search_strategy.png|600px]]<br />
</div><br />
<br />
There are some common search strategies used in the field of NAS, such as Reinforcement learning, Random search, Evolution algorithm, and Grid Search.<br />
<br />
The one they used in the paper is Random Search. It basically samples points from the search space uniformly at random as well as sampling<br />
some points that are close to the current observed best point. Intuitively it makes sense because it combines exploration and exploitation. When you sample points close to the current<br />
optimal point, you are doing exploitation. And when you sample points randomly, you are doing exploration.<br />
<br />
The pseudocode for a general random search algorithm is provided below.<br />
<br />
[[File:Pseudoc.png |700px|center]]<br />
<br />
It essentially repeatedly searches randomly within the hypersphere of the current state, and updates only if the reward function is increased when using the newly found vector. The approach is highly non-parametric, and is easily generalized for complex problems such as architectural finding once parameters are properly defined. Although Random Search can return a reasonable approximation of the optimal solution under low problem dimensionality, the approach is commonly cited to perform poorly under higher problem dimensionality. The implementation of Random Search within this context is used to find highly complex architectures with millions of parameters; this could explain the only marginal improvements to human created state-of-the art networks despite the heavy machinery used to arrive at new architectures in the experiments section.<br />
<br />
They quoted from another paper that claims random search performs the random search is competitive with reinforcement learning and other learning techniques [7]. In the implementation, they used Google's black box optimization tool Google vizier. It is not open source, but there is an open source implementation of it [8]. A more recent and detailed survey detailing other methods such as Bayesian optimization strategies for Neural architecture search can be found in [13]<br />
<br />
=Performance Evaluation Strategy=<br />
<br />
The evaluation in this particular task is very tricky. The reason is we are evaluating neural network here. In order to evaluate it, we need to train it first. And we are doing pixel level classification on images with high resolutions, so the naive approach would require a tremendous amount of computational resources. <br />
<br />
The way they solve it in the paper is defining a proxy task. The proxy task is a task that requires sufficient less computational resources, while can still give a good estimate of the performance of the network. In most image classical tasks of NAS, the proxy<br />
task is to train the network on images of lower resolution. The assumption is, if the network performs well on images with lower density, it should reasonably perform well on images with higher resolution.<br />
<br />
However, the above approach does not work on this case. The reason is that the dense prediction tasks innately require high-resolution images as training data. The approach used in the paper is the flowing:<br />
<ol><br />
<li> Use a smaller backbone for proxy task</li><br />
<li> caching the feature maps produced by the network backbone on the training set and directly building a single DPC on top of it </li><br />
<li> Early stopping train for 30k iterations with a batch size of 8</li><br />
</ol><br />
<br />
If training on the large-scale backbone without fixing the weights of the backbone, they would need one week to train a network on a P100 GPU, but now they cut down the proxy task to be run 90 min. Then they rank the selected architectures, choosing the top 50 and do <br />
a full evaluation on it.<br />
<br />
The evaluation metric they used is called mIOU, which is pixel level intersection over union. Which just the area of the intersection<br />
of the ground truth and the prediction over the area of the union of the ground truth and the prediction.<br />
<br />
=Result=<br />
<br />
This method achieves state of art performances in many datasets. The following table quantifies the gain on performance on many datasets.<br />
<br />
<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">[[File:Screen Shot 2018-11-10 at 6.51.14 PM.png| 800px]]<br />
</div><br />
The chose to train on modified Xception network as a backbone, and the following are the resulting architecture for the DPC.<br />
<br />
<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">[[File:Screen Shot 2018-11-12 at 12.32.05 PM.png|1000px]]<br />
</div><br />
<br />
Table 2 describes the results on scene parsing dataset. It sets a new state-of-the-art performance of 82.7% mIOU and outperforms other state-of-the-art models across 11 of the 19 categories.<br />
<br />
Table 3 describes the results on person part segmentation dataset. It achieve the state-of-the-art performance of 71.34% mIOU and outperforms other state-of-the-art models across 6 of the 7 categories.<br />
<br />
Table 4 describes the results on semantic image segmentation dataset. It achieve the state-of-the-art performance of 87.9% mIOU and outperforms other state-of-the-art models across 6 of the 20 categories.<br />
<br />
As we can see, the searched DPC model achieves better performance (measured by mIOU) with less than half of the computational resources(parameters), and 37% less of operations (add and multiply).<br />
<br />
=Future work=<br />
The author suggests that when increasing the number of branches in the DPC, there might be a further gain on the performance on the<br />
image segmentation task. However, although the random search in an exponentially growing space may become more challenging. There may need more intelligent search strategy. They hope that by using some meta learning on metadata it can lead to future insight and be advantageous. <br />
<br />
The author hope that this architecture search techniques can be ported into other domains such as depth prediction and object detection to achieve similar gains over human-invented designs.<br />
<br />
=Critique=<br />
<br />
1. Rich man's game<br />
<br />
The technique described in the paper can only be applied by parties with abundant computational resources, like Google, Facebook, Microsoft, and e.t.c. For small research groups and companies, this method is not that useful due to the lack of computational power. Future improvement will be needed on the design an even more efficient proxy task that can tell whether a network will perform<br />
well that requires fewer computations. <br />
<br />
2. Benefit/Cost ratio<br />
<br />
The technique here does outperform human designed network in many cases, but the gain is not huge. In Cityscapes dataset, the performance gain is 0.7%, wherein PASCAL-Person-Part dataset, the gain is 3.7%, and the PASCAL VOC 2012 dataset, it does not outperform human experts. (All measured by mIOU) Even though the push of the state-of-the-art is always something that worth celebrating, <br />
but in practice, one would argue after spending so many resources doing the search, the computer should achieve superhuman performance. (Like Chess Engine vs Chess Grand Master). In practice, one may simply go with the current state-of-the-art model to avoid the expensive search cost.<br />
<br />
3. Still Heavily influenced by Human Bias<br />
<br />
When we define the search space, we introduced human bias. Firstly, the network backbone is chosen from previous matured architectures, which may not actually be optimal. Secondly, the internal branches in the DPC also consist with layers whose operations are defined by us humans, and we define these operations based on previous experience. That also prevents the search algorithm to find something revolutionary.<br />
<br />
4. May have the potential to take away entry-level data science jobs.<br />
<br />
If there is a significant reduction in the search cost, it will be more cost effective to apply NAS rather than hire data scientists. Once matured, this technology will have the potential to take away entry-level data science jobs and make data science jobs only possessed by high-level researchers. <br />
<br />
There are some real-world applications that already deploy NAS techniques in production. Two good examples are Google AutoML and Microsoft Custom Vision AI.<br />
[9, 10]<br />
<br />
=References=<br />
1. Searching For Efficient Multi-Scale Architectures For Dense Image Prediction, [[https://arxiv.org/abs/1809.04184]].<br />
<br />
2. E. Real, A. Aggarwal, Y. Huang, and Q. V. Le. Regularized evolution for image classifier architecture search. arXiv:1802.01548, 2018.<br />
<br />
3. C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L.-J. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy. Progressive neural architecture search. In ECCV, 2018.<br />
<br />
4. B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le. Learning transferable architectures for scalable image recognition. In CVPR, 2018.<br />
<br />
5. Neural Architecture Search: A Survey [[https://arxiv.org/abs/1808.05377]]<br />
<br />
6. Deep Residual Learning for Image Recognition [[https://arxiv.org/pdf/1512.03385.pdf]]<br />
<br />
7. J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In CVPR, 2015.<br />
In the implementation wise, they used a Google vizier, which is a search tool for black box optimization. [D. Golovin, B. Solnik, S. Moitra, G. Kochanski, J. Karro, and D. Sculley. Google vizier: A service for black-box optimization. In SIGKDD, 2017.]<br />
<br />
8. Github implementation of Google Vizer, a black-box optimization tool [https://github.com/tobegit3hub/advisor.]<br />
<br />
9. AutoML: https://cloud.google.com/automl/ <br />
<br />
10. Custom-vision: https://azure.microsoft.com/en-us/services/cognitive-services/custom-vision-service/<br />
<br />
11. J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei. Deformable convolutional networks. In ICCV, 2017.<br />
<br />
12. M. Yang, K. Yu, C. Zhang, Z. Li, and K. Yang. Denseaspp for semantic segmentation in street scenes. In CVPR, 2018.<br />
<br />
13. Elsken, Thomas, Jan Hendrik Metzen, and Frank Hutter. "Neural architecture search: A survey." arXiv preprint arXiv:1808.05377 (2018).</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:picture2.png&diff=42335File:picture2.png2018-12-08T01:39:14Z<p>Aghabuss: </p>
<hr />
<div></div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Robot_Learning_in_Homes:_Improving_Generalization_and_Reducing_Dataset_Bias&diff=42334Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias2018-12-08T01:36:30Z<p>Aghabuss: /* Performance on Real Sawyer */</p>
<hr />
<div>==Introduction==<br />
<br />
The use of data-driven approaches in robotics has increased in the last decade. Instead of using hand-designed models, these data-driven approaches work on large-scale datasets and learn appropriate policies that map from high-dimensional observations to actions. Since collecting data using an actual robot in real-time is very expensive, most of the data-driven approaches in robotics use simulators in order to collect simulated data. The concern here is whether these approaches have the capability to be robust enough to domain shift and to be used for real-world data. It is an undeniable fact that there is a wide reality gap between simulators and the real world.<br />
<br />
This has motivated the robotics community to increase their efforts in collecting real-world physical interaction data for a variety of tasks. This effort has been accelerated by the declining costs of hardware. This approach has been quite successful at tasks such as grasping, pushing, poking and imitation learning. However, the major problem is that the performance of these learning models are not good enough and tend to plateau fast. Furthermore, robotic action data did not lead to similar gains in other areas such as computer vision and natural language processing. As the paper claimed, the solution for all of these obstacles is using “real data”. Current robotic datasets lack diversity of environment. Learning-based approaches need to move out of simulators in the labs and go to real environments such as real homes so that they can learn from real datasets. <br />
<br />
Like every other process, the process of collecting real-world data is made difficult by a number of problems. First, there is a need for cheap and compact robots to collect data in homes but current industrial robots (i.e. Sawyer and Baxter) are too expensive. Secondly, cheap robots are not accurate enough to collect reliable data. Also, there is a lack of constant supervision for data collection in homes. Finally, there is also a circular dependency problem in home-robotics: there is a lack of real-world data which are needed to improve current robots, but current robots are not good enough to collect reliable data in homes. These challenges in addition to some other external factors will likely result in noisy data collection. In this paper, a first systematic effort has been presented for collecting a dataset inside homes. In accomplishing this goal, the authors: <br />
<br />
1. Build a cheap robot costing less than USD 3K which is appropriate for use in homes<br />
<br />
2. Collect training data in 6 different homes and testing data in 3 homes<br />
<br />
3. Propose a method for modelling the noise in the labelled data<br />
<br />
4. Demonstrate that the diversity in the collected data provides superior performance and requires little-to-no domain adaptation<br />
<br />
[[File:aa1.PNG|600px|thumb|center|]]<br />
<br />
==Overview==<br />
<br />
This paper emphasizes the importance of diversifying the data for robotic learning in order to have a greater generalization, by focusing on the task of grasping. A diverse dataset also allows for removing biases in the data. By considering these facts, the paper argues that even for simple tasks like grasping, datasets which are collected in labs suffer from strong biases such as simple backgrounds and same environment dynamics. Hence, the learning approaches cannot generalize the models and work well on real datasets.<br />
<br />
As a future possibility, there would be a need for having a low-cost robot to collect large-scale data inside a huge number of homes. For this reason, they introduced a customized mobile manipulator. They used a Dobot Magician which is a robotic arm mounted on a Kobuki which is a low-cost mobile robot base equipped with sensors such as bumper contact sensors and wheel encoders. The resulting robot arm has five degrees of freedom (DOF) (x, y, z, roll, pitch). The gripper is a two-fingered electric gripper with a 0.3kg payload. They also add an Intel R200 RGBD camera to their robot which is at a height of 1m above the ground. An Intel Core i5 processor is also used as an onboard laptop to perform all the processing. The whole system can run for 1.5 hours with a single charge.<br />
<br />
As there is always a trade-off, when we gain a low-cost robot, we are actually losing accuracy for controlling it. So, the low-cost robot which is built from cheaper components than the expensive setups such as Baxter and Sawyer suffers from higher calibration errors and execution errors. This means that the dataset collected with this approach is diverse and huge but it has noisy labels. To illustrate, consider when the robot wants to grasp at location <math> {(x, y)}</math>. Since there is a noise in the execution, the robot may perform this action in the location <math> {(x + \delta_{x}, y+ \delta_{y})}</math> which would assign the success or failure label of this action to a wrong place. Therefore, to solve the problem, they used an approach to learn from noisy data. They modeled noise as a latent variable and used two networks, one for predicting the noise and one for predicting the action to execute.<br />
<br />
==Learning on low-cost robot data==<br />
<br />
This paper uses a patch grasping framework in its proposed architecture. Also, as mentioned before, there is a high tendency for noisy labels in the datasets which are collected by inaccurate and cheap robots. The cause of the noise in the labels could be due to the hardware execution error, inaccurate kinematics, camera calibration, proprioception, wear, and tear, etc. Here are more explanations about different parts of the architecture in order to disentangle the noise of the low-cost robot’s actual and commanded executions.<br />
<br />
===Grasping Formulation===<br />
<br />
Planar grasping is the object of interest in this architecture. It means that all the objects are grasped at the same height and vertical to the ground (ie: a fixed end-effector pitch). The object is fixed in the z direction and basically perpendicular to the ground. The final goal is to find <math>{(x, y, \theta)}</math> given an observation <math> {I}</math> of the object, where <math> {x}</math> and <math> {y}</math> are the translational degrees of freedom and <math> {\theta}</math> is the rotational degrees of freedom (roll of the end-effector). For the purpose of comparison, they used a model which does not predict the <math>{(x, y, \theta)}</math> directly from the image <math> {I}</math>, but samples several smaller patches <math> {I_{P}}</math> at different locations <math>{(x, y)}</math>. Thus, the angle of grasp <math> {\theta}</math> is predicted from these patches. Also, in order to have multi-modal predictions, discrete steps of the angle <math> {\theta}</math>, <math> {\theta_{D}}</math> is used. <br />
<br />
Hence, each datapoint consists of an image <math> {I}</math>, the executed grasp <math>{(x, y, \theta)}</math> and the grasp success/failure label g. Then, the image <math> {I}</math> and the angle <math> {\theta}</math> are converted to image patch <math> {I_{P}}</math> and angle <math> {\theta_{D}}</math>. Then, to minimize the classification error, a binary cross entropy loss is used which minimizes the error between the predicted and ground truth label <math> g </math>. A convolutional neural network with weight initialization from pre-training on Imagenet is used for this formulation.<br />
<br />
(Note: On Cross Entropy:<br />
<br />
If we think of a distribution as the tool we use to encode symbols, then entropy measures the number of bits we'll need if we use the correct tool. This is optimal, in that we can't encode the symbols using fewer bits on average.<br />
In contrast, cross entropy is the number of bits we'll need if we encode symbols from <math>y</math> using the wrong tool <math> {\hat h}</math> . This consists of encoding the <math> {i_{th}}</math> symbol using <math> {\log(\frac{1}{{\hat h_i}})}</math> bits instead of <math> {\log(\frac{1}{{ h_i}})}</math> bits. We of course still take the expected value to the true distribution y , since it's the distribution that truly generates the symbols:<br />
<br />
\begin{align}<br />
H(y,\hat y) = \sum_i{y_i\log{\frac{1}{\hat y_i}}}<br />
\end{align}<br />
<br />
Cross entropy is always larger than entropy; encoding symbols according to the wrong distribution <math> {\hat y}</math> will always make us use more bits. The only exception is the trivial case where y and <math> {\hat y}</math> are equal, and in this case entropy and cross entropy are equal.)<br />
<br />
===Modeling noise as latent variable===<br />
<br />
In order to tackle the problem of inaccurate position control and calibration due to cheap robot, they found a structure in the noise which is dependent on the robot and the design. They modeled this structure of noise as a latent variable and decoupled during training. The approach is shown in figure 2: <br />
<br />
<br />
[[File:aa2.PNG|600px|thumb|center|]]<br />
<br />
The conventional approach models the grasp success probability for a given image patch at a given angle where the variables of the environment which can introduce noise in the system is generally insignificant, due to the high accuracy of expensive, commercial robots. However, in the low cost setting with multiple robots collecting data in parallel, it becomes an important consideration for learning. <br />
<br />
The grasp success probability for image patch <math> {I_{P}}</math> at angle <math> {\theta_{D}}</math> is represented as <math> {P(g|I_{P},\theta_{D}; \mathcal{R} )}</math> where <math> \mathcal{R}</math> represents environment variables that can add noise to the system.<br />
<br />
The conditional probability of grasping at a noisy image patch <math>I_P</math> for this model is computed by:<br />
<br />
<br />
\[ { P(g|I_{P},\theta_{D}, \mathcal{R} ) = ∑_{( \widehat{I_P} \in \mathcal{P})} P(g│z=\widehat{I_P},\theta_{D},\mathcal{R}) \cdot P(z=\widehat{I_P} | \theta_{D},I_P,\mathcal{R})} \]<br />
<br />
<br />
Here, <math> {z}</math> models the latent variable of the actual patch executed, and <math>\widehat{I_P}</math> belongs to a set of possible neighboring patches <math> \mathcal{P}</math>.<math> P(z=\widehat{I_P}|\theta_D,I_P,\mathcal{R})</math> shows the noise which can be caused by <math>\mathcal{R}</math> variables and is implemented as the Noise Modelling Network (NMN). <math> {P(g│z=\widehat{I_P},\theta_{D}, \mathcal{R} )}</math> shows the grasp prediction probability given the true patch and is implemented as the Grasp Prediction Network (GPN). The overall Robust-Grasp model is computed by marginalizing GPN and NMN.<br />
<br />
===Learning the latent noise model===<br />
<br />
This section concerns what be the inputs to the NMN network should be and how should the inputs can be trained. The authors assume that <math> {z}</math> is conditionally independent of the local patch-specific variables <math> {(I_{P}, \theta_{D})}</math>. To estimate the latent variable <math> {z}</math> given the global information <math>\mathcal{R}</math>, i.e <math> P(z=\widehat{I_P}|\theta_D,I_P,\mathcal{R}) \equiv P(z=\widehat{I_P}|\mathcal{R})</math>. Apart from the patch <math> I_{P} </math> and grasp information <math>(x, y, θ)</math>, they use information like image of the entire scene, ID of the robot and the location of the raw pixel. They argue that the image of the full scene could contain some essential information about the system such as the relative location of camera to the ground which may change over the lifetime of the robot. The identification number of the robot might give cues about errors specific to a particular hardware. Finally, the raw pixels of execution contain calibration specific information, since calibration error is coupled with pixel location, since least squares fit are used to to compute calibration parameters.<br />
<br />
They used direct optimization to learn both NMN and GPN with noisy labels. However, explicit labels are not available to train NMN but the latent variable <math>z</math> can be estimated using a technique such as Expectation-Maximization. The entire image of the scene and the environment information are the inputs of the NMN, as well as robot ID and raw-pixel grasp location. The output of the NMN is the probability distribution of the actual patches where the grasps are executed. Finally, a binary cross entropy loss is applied to the marginalized output of these two networks and the true grasp label <math>g</math>.<br />
<br />
===Training details===<br />
<br />
They implemented their model in PyTorch and fine tuned a pretrained ResNet-18 model. They concatenated 512 dimensional ResNet feature with a 1-hot vector of robot ID and the raw pixel location of the grasp for their NMN. This passes through a series of three fully connected layers and a SoftMax layer to convert the correct patch predictions to a probability distribution. Also, the inputs of the GPN are the original noisy patch plus 8 other equidistant patches from the original one. The angle predictions for all the patches are passed through a sigmoid activation at the end to obtain grasp success probability for a specific patch at a specific angle.<br />
<br />
The training of the network takes place in two stages. It starts with training only GPN over 5 epochs of the data. Then, the NMN and the marginalization operator are added to the model. So, they train NMN and GPN simultaneously in an end-to-end fashion for the other 25 epochs.<br />
<br />
This two-stage approach is crucial for effective training of their networks, without which NMN trivially selects the same patch irrespective of the input. The optimizer used for training is Adam [16].<br />
<br />
==Results==<br />
<br />
In the results part of the paper, they show that collecting dataset in homes is essential for generalizing learning from unseen environments. They also show that modelling the noise in their Low-Cost Arm (LCA) can improve grasping performance.<br />
<br />
They collected data in parallel using multiple robots in 6 different homes, as shown in Figure 3. They used an object detector (tiny-YOLO) as the input data were unstructured due to LCA limited memory and computational capabilities. With an object location detected, class information was discarded, and a grasp was attempted. The grasp location in 3D was computed using PointCloud data. They scattered different objects in homes within 2m area to prevent collision of the robot with obstacles and let the robot move randomly and grasp objects. Finally, they collected a dataset with 28K grasp results.<br />
<br />
[[File:aa3.PNG|600px|thumb|center|]]<br />
<br />
To evaluate their approach in a more quantitative way, they used three test settings:<br />
<br />
- The first one is a binary classification or held-out data. The test set is collected by performing random grasps on objects. They measure the performance of binary classification by predicting the success or failure of grasping, given a location and the angle. Using binary classification allows for testing a lot of models without running them on real robots. They collected two held-out datasets using LCA in lab and homes and the dataset for Baxter robot.<br />
<br />
- The second one is Real Low-Cost Arm (Real-LCA). Here, they evaluate their model by running it in three unseen homes. They put 20 new objects in these three homes in different orientations. Since the objects and the environments are completely new, this tests could measure the generalization of the model.<br />
<br />
- The third one is Real Sawyer (Real-Sawyer). They evaluate the performance of their model by running the model on the Sawyer robot which is more accurate than the LCA. They tested their model in the lab environment to show that training models with the datasets collected from homes can improve the performance of models even in lab environments.<br />
<br />
They used baselines for both their data which is collected in homes and their model which is Robust-Grasp. They used two datasets for the baseline. The dataset collected by (Lab-Baxter) and the dataset collected by their LCA in the lab (Lab-LCA).<br />
They compared their Robust-Grasp model with the noise independent patch grasping model (Patch-Grasp) [4]. They also compared their data and model with DexNet-3.0 (DexNet) for a strong real-world grasping baseline.<br />
<br />
===Experiment 1: Performance on held-out data===<br />
<br />
Table 1 shows that the models trained on lab data cannot generalize to the Home-LCA environment (i.e. they overfit to their respective environments and attain a lower binary classification score). However, the model trained on Home-LCA has a good performance on both lab data and home environment.<br />
<br />
[[File:aa4.PNG|600px|thumb|center|]]<br />
<br />
===Experiment 2: Performance on Real LCA Robot===<br />
<br />
In table 2, the performance of the Home-LCA is compared against a pre-trained DexNet and the model trained on the Lab-Baxter. Training on the Home-LCA dataset performs 43.7% better than training on the Lab-Baxter dataset and 33% better than DexNet. The low performance of DexNet can be described by the possible noise in the depth images that are caused by the natural light. DexNet, which requires high-quality depth sensing, cannot perform well in these scenarios. By using cheap commodity RGBD cameras in LCA, the noise in the depth images is not a matter of concern, as the model has no expectation of high-quality sensing.<br />
<br />
[[File:aa5.PNG|600px|thumb|center|]]<br />
<br />
===Performance on Real Sawyer===<br />
<br />
To compare the performance of the Robust-Grasp model against the Patch-Grasp model without collecting noise-free data, they used Lab-Baxter for benchmarking, which is an accurate and better-calibrated robot. The Sawyer robot is used for testing to ensure that the testing robot is different from both training robots. As shown in Table 3, the Robust-Grasp model trained on Home-LCA outperforms the Patch-Grasp model and achieves 77.5% accuracy which is similar to several recent learning to grasp papers. This accuracy is similar to several recent papers, however, this model was trained and tested in a different environment. The Robust-Grasp model also outperforms the Patch-Grasp by about 4% on binary classification. Furthermore, the visualizations of predicted noise corrections in Figure 4 shows that the corrections depend on both the pixel locations of the noisy grasp and the robot.<br />
<br />
[[File:aa6.PNG|600px|thumb|center|]]<br />
<br />
[[File:aa7.PNG|600px|thumb|center|]]<br />
<br />
==Related work==<br />
<br />
Over the last few years, the interest of scaling up robot learning with large-scale datasets has been increased. Hence, many papers were published in this area. A hand annotated grasping dataset, a self-supervised grasping dataset, and grasping using reinforcement learning are some examples of using large-scale datasets for grasping. The work mentioned above used high-cost hardware and data labeling mechanisms. There were also many papers that worked on other robotic tasks like material recognition, pushing objects and manipulating a rope. However, none of these papers worked on real data in real environments like homes, they all used lab data.<br />
<br />
Furthermore, since grasping is one of the basic problems in robotics, there were some efforts to improve grasping. Classical approaches focused on physics-based issues of grasping and required 3D models of the objects. However, recent works focused on data-driven approaches which learn from visual observations to grasp objects. Simulation and real-world robots are both required for large-scale data collection. A versatile grasping model was proposed to achieve a 90% performance for a bin-picking task. The point here is that they usually require high-quality depth as input which seems to be a barrier for practical use of robots in real environments. High-quality depth sensing means a high cost to implement in hardware and thus is a barrier for practical use.<br />
<br />
Most labs use industrial robots or standard collaborative hardware for their experiments. Therefore, there is few research that used low-cost robots. One of the examples is learning using a cheap inaccurate robot for stack multiple blocks. Although mobile robots like iRobot’s Roomba have been in the home consumer electronics market for a decade, it is not clear whether learning approaches are used in it alongside mapping and planning.<br />
<br />
Learning from noisy inputs is another challenge specifically in computer vision. A controversial question which is often raised in this area is whether learning from noise can improve the performance. Some works show it could have bad effects on the performance; however, some other works find it valuable when the noise is independent or statistically dependent on the environment. In this paper, they used a model that can exploit the noise and learn a better grasping model.<br />
<br />
==Conclusion==<br />
<br />
All in all, the paper presents an approach for collecting large-scale robot data in real home environments. They implemented their approach by using a mobile manipulator which is a lot cheaper than the existing industrial robots and costs under 3K USD. They collected a dataset of 28K grasps in six different homes. In order to solve the problem of noisy labels which were caused by their inaccurate robots, they presented a framework to factor out the noise in the data. They tested their model by physically grasping 20 new objects in three new homes and in the lab. The model trained with home dataset showed 43.7% improvement over the models trained with lab data. Their framework performed 33% better than a baseline DexNet model, which struggled with the typically poor depth sensing in common household environments with a lot of natural light. Their results also showed that their model can improve the grasping performance even in lab environments. They also demonstrated that their architecture for modeling the noise improved the performance by about 10%.<br />
<br />
==Critiques==<br />
<br />
This paper does not contain a significant algorithmic contribution. They are just combining a large number of data engineering techniques for the robot learning problem. The authors claim that they have obtained 43.7% more accuracy than baseline models, but it does not seem to be a fair comparison as the data collection happened in simulated settings in the lab for other methods, whereas the authors use the home dataset. The authors must have also discussed safety issues when training robots in real environments as against simulated environments like labs. The authors are encouraging other researchers to look outside the labs, but are not discussing the critical safety issues in this approach.<br />
<br />
Another strange finding is that the paper mentions that they "follow a model architecture similar to [Pinto and Gupta [4]]," however, the proposed model is, in fact, a fine-tuned resnet-18 architecture. Pinto and Gupta, implement a version similar to AlexNet as shown below in Figure 5.<br />
<br />
[[File:Figure_5_PandG.JPG | 450px|thumb|center|Figure 5: AlexNet architecture implemented in Pinto and Gupta [4].]]<br />
<br />
<br />
The paper argues that the dataset collected by the LCA is noisy, since the robot is cheap and inaccurate. It further asserts that in order to handle the noise in the dataset, they can model the noise as a latent variable and their model can improve the performance of grasping. Although learning from noisy data and achieving a good performance is valuable, it is better that they test their noise modeling network for other robots as well. Since their noise modelling network takes robot information as an input, it would be a good idea to generalize it by testing it using different inaccurate robots to ensure that it would perform well.<br />
<br />
They did not mention other aspects of their comparison, for example they could mention their training time compared to other models or the size of other datasets.<br />
<br />
==References==<br />
<br />
#Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel. "Domain randomization for transferring deep neural networks from simulation to the real world." 2017. URL https://arxiv.org/abs/1703.06907.<br />
#Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel. "Sim-to-real transfer of robotic control with dynamics randomization." arXiv preprint arXiv:1710.06537,2017.<br />
#Lerrel Pinto, Marcin Andrychowicz, Peter Welinder, Wojciech Zaremba, and Pieter Abbeel. "Asymmetric actor-critic for image-based robot learning." Robotics Science and Systems, 2018.<br />
#Lerrel Pinto and Abhinav Gupta. "Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours." CoRR, abs/1509.06825, 2015. URL http://arxiv.org/abs/1509. 06825.<br />
#Adithyavairavan Murali, Lerrel Pinto, Dhiraj Gandhi, and Abhinav Gupta. "CASSL: Curriculum accelerated self-supervised learning." International Conference on Robotics and Automation, 2018.<br />
# Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel. "End-to-end training of deep visuomotor policies." The Journal of Machine Learning Research, 17(1):1334–1373, 2016.<br />
#Sergey Levine, Peter Pastor, Alex Krizhevsky, and Deirdre Quillen. "Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection." CoRR, abs/1603.02199, 2016. URL http://arxiv.org/abs/1603.02199.<br />
#Pulkit Agarwal, Ashwin Nair, Pieter Abbeel, Jitendra Malik, and Sergey Levine. "Learning to poke by poking: Experiential learning of intuitive physics." 2016. URL http://arxiv.org/ abs/1606.07419<br />
#Chelsea Finn, Ian Goodfellow, and Sergey Levine. "Unsupervised learning for physical interaction through video prediction." In Advances in neural information processing systems, 2016.<br />
#Ashvin Nair, Dian Chen, Pulkit Agrawal, Phillip Isola, Pieter Abbeel, Jitendra Malik, and Sergey Levine. "Combining self-supervised learning and imitation for vision-based rope manipulation." International Conference on Robotics and Automation, 2017.<br />
#Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta. "Revisiting unreasonable effectiveness of data in deep learning era." ICCV, 2017.<br />
#Marc Peter Deisenroth, Carl Edward Rasmussen, and Dieter Fox. Learning to control a low-cost manipulator using data-efficient reinforcement learning. RSS, 2011.<br />
#David F Nettleton, Albert Orriols-Puig, and Albert Fornells. A study of the effect of different types of noise on the precision of supervised learning techniques. Artificial intelligence review, 33(4):275–306, 2010.<br />
#Benoît Frénay and Michel Verleysen. Classification in the presence of label noise: a survey. IEEE transactions on neural networks and learning systems, 25(5):845–869, 2014.<br />
#Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang. Learning from massive noisy labeled data for image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2691–2699, 2015.<br />
#Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Robot_Learning_in_Homes:_Improving_Generalization_and_Reducing_Dataset_Bias&diff=42333Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias2018-12-08T01:33:30Z<p>Aghabuss: /* Conclusion */</p>
<hr />
<div>==Introduction==<br />
<br />
The use of data-driven approaches in robotics has increased in the last decade. Instead of using hand-designed models, these data-driven approaches work on large-scale datasets and learn appropriate policies that map from high-dimensional observations to actions. Since collecting data using an actual robot in real-time is very expensive, most of the data-driven approaches in robotics use simulators in order to collect simulated data. The concern here is whether these approaches have the capability to be robust enough to domain shift and to be used for real-world data. It is an undeniable fact that there is a wide reality gap between simulators and the real world.<br />
<br />
This has motivated the robotics community to increase their efforts in collecting real-world physical interaction data for a variety of tasks. This effort has been accelerated by the declining costs of hardware. This approach has been quite successful at tasks such as grasping, pushing, poking and imitation learning. However, the major problem is that the performance of these learning models are not good enough and tend to plateau fast. Furthermore, robotic action data did not lead to similar gains in other areas such as computer vision and natural language processing. As the paper claimed, the solution for all of these obstacles is using “real data”. Current robotic datasets lack diversity of environment. Learning-based approaches need to move out of simulators in the labs and go to real environments such as real homes so that they can learn from real datasets. <br />
<br />
Like every other process, the process of collecting real-world data is made difficult by a number of problems. First, there is a need for cheap and compact robots to collect data in homes but current industrial robots (i.e. Sawyer and Baxter) are too expensive. Secondly, cheap robots are not accurate enough to collect reliable data. Also, there is a lack of constant supervision for data collection in homes. Finally, there is also a circular dependency problem in home-robotics: there is a lack of real-world data which are needed to improve current robots, but current robots are not good enough to collect reliable data in homes. These challenges in addition to some other external factors will likely result in noisy data collection. In this paper, a first systematic effort has been presented for collecting a dataset inside homes. In accomplishing this goal, the authors: <br />
<br />
1. Build a cheap robot costing less than USD 3K which is appropriate for use in homes<br />
<br />
2. Collect training data in 6 different homes and testing data in 3 homes<br />
<br />
3. Propose a method for modelling the noise in the labelled data<br />
<br />
4. Demonstrate that the diversity in the collected data provides superior performance and requires little-to-no domain adaptation<br />
<br />
[[File:aa1.PNG|600px|thumb|center|]]<br />
<br />
==Overview==<br />
<br />
This paper emphasizes the importance of diversifying the data for robotic learning in order to have a greater generalization, by focusing on the task of grasping. A diverse dataset also allows for removing biases in the data. By considering these facts, the paper argues that even for simple tasks like grasping, datasets which are collected in labs suffer from strong biases such as simple backgrounds and same environment dynamics. Hence, the learning approaches cannot generalize the models and work well on real datasets.<br />
<br />
As a future possibility, there would be a need for having a low-cost robot to collect large-scale data inside a huge number of homes. For this reason, they introduced a customized mobile manipulator. They used a Dobot Magician which is a robotic arm mounted on a Kobuki which is a low-cost mobile robot base equipped with sensors such as bumper contact sensors and wheel encoders. The resulting robot arm has five degrees of freedom (DOF) (x, y, z, roll, pitch). The gripper is a two-fingered electric gripper with a 0.3kg payload. They also add an Intel R200 RGBD camera to their robot which is at a height of 1m above the ground. An Intel Core i5 processor is also used as an onboard laptop to perform all the processing. The whole system can run for 1.5 hours with a single charge.<br />
<br />
As there is always a trade-off, when we gain a low-cost robot, we are actually losing accuracy for controlling it. So, the low-cost robot which is built from cheaper components than the expensive setups such as Baxter and Sawyer suffers from higher calibration errors and execution errors. This means that the dataset collected with this approach is diverse and huge but it has noisy labels. To illustrate, consider when the robot wants to grasp at location <math> {(x, y)}</math>. Since there is a noise in the execution, the robot may perform this action in the location <math> {(x + \delta_{x}, y+ \delta_{y})}</math> which would assign the success or failure label of this action to a wrong place. Therefore, to solve the problem, they used an approach to learn from noisy data. They modeled noise as a latent variable and used two networks, one for predicting the noise and one for predicting the action to execute.<br />
<br />
==Learning on low-cost robot data==<br />
<br />
This paper uses a patch grasping framework in its proposed architecture. Also, as mentioned before, there is a high tendency for noisy labels in the datasets which are collected by inaccurate and cheap robots. The cause of the noise in the labels could be due to the hardware execution error, inaccurate kinematics, camera calibration, proprioception, wear, and tear, etc. Here are more explanations about different parts of the architecture in order to disentangle the noise of the low-cost robot’s actual and commanded executions.<br />
<br />
===Grasping Formulation===<br />
<br />
Planar grasping is the object of interest in this architecture. It means that all the objects are grasped at the same height and vertical to the ground (ie: a fixed end-effector pitch). The object is fixed in the z direction and basically perpendicular to the ground. The final goal is to find <math>{(x, y, \theta)}</math> given an observation <math> {I}</math> of the object, where <math> {x}</math> and <math> {y}</math> are the translational degrees of freedom and <math> {\theta}</math> is the rotational degrees of freedom (roll of the end-effector). For the purpose of comparison, they used a model which does not predict the <math>{(x, y, \theta)}</math> directly from the image <math> {I}</math>, but samples several smaller patches <math> {I_{P}}</math> at different locations <math>{(x, y)}</math>. Thus, the angle of grasp <math> {\theta}</math> is predicted from these patches. Also, in order to have multi-modal predictions, discrete steps of the angle <math> {\theta}</math>, <math> {\theta_{D}}</math> is used. <br />
<br />
Hence, each datapoint consists of an image <math> {I}</math>, the executed grasp <math>{(x, y, \theta)}</math> and the grasp success/failure label g. Then, the image <math> {I}</math> and the angle <math> {\theta}</math> are converted to image patch <math> {I_{P}}</math> and angle <math> {\theta_{D}}</math>. Then, to minimize the classification error, a binary cross entropy loss is used which minimizes the error between the predicted and ground truth label <math> g </math>. A convolutional neural network with weight initialization from pre-training on Imagenet is used for this formulation.<br />
<br />
(Note: On Cross Entropy:<br />
<br />
If we think of a distribution as the tool we use to encode symbols, then entropy measures the number of bits we'll need if we use the correct tool. This is optimal, in that we can't encode the symbols using fewer bits on average.<br />
In contrast, cross entropy is the number of bits we'll need if we encode symbols from <math>y</math> using the wrong tool <math> {\hat h}</math> . This consists of encoding the <math> {i_{th}}</math> symbol using <math> {\log(\frac{1}{{\hat h_i}})}</math> bits instead of <math> {\log(\frac{1}{{ h_i}})}</math> bits. We of course still take the expected value to the true distribution y , since it's the distribution that truly generates the symbols:<br />
<br />
\begin{align}<br />
H(y,\hat y) = \sum_i{y_i\log{\frac{1}{\hat y_i}}}<br />
\end{align}<br />
<br />
Cross entropy is always larger than entropy; encoding symbols according to the wrong distribution <math> {\hat y}</math> will always make us use more bits. The only exception is the trivial case where y and <math> {\hat y}</math> are equal, and in this case entropy and cross entropy are equal.)<br />
<br />
===Modeling noise as latent variable===<br />
<br />
In order to tackle the problem of inaccurate position control and calibration due to cheap robot, they found a structure in the noise which is dependent on the robot and the design. They modeled this structure of noise as a latent variable and decoupled during training. The approach is shown in figure 2: <br />
<br />
<br />
[[File:aa2.PNG|600px|thumb|center|]]<br />
<br />
The conventional approach models the grasp success probability for a given image patch at a given angle where the variables of the environment which can introduce noise in the system is generally insignificant, due to the high accuracy of expensive, commercial robots. However, in the low cost setting with multiple robots collecting data in parallel, it becomes an important consideration for learning. <br />
<br />
The grasp success probability for image patch <math> {I_{P}}</math> at angle <math> {\theta_{D}}</math> is represented as <math> {P(g|I_{P},\theta_{D}; \mathcal{R} )}</math> where <math> \mathcal{R}</math> represents environment variables that can add noise to the system.<br />
<br />
The conditional probability of grasping at a noisy image patch <math>I_P</math> for this model is computed by:<br />
<br />
<br />
\[ { P(g|I_{P},\theta_{D}, \mathcal{R} ) = ∑_{( \widehat{I_P} \in \mathcal{P})} P(g│z=\widehat{I_P},\theta_{D},\mathcal{R}) \cdot P(z=\widehat{I_P} | \theta_{D},I_P,\mathcal{R})} \]<br />
<br />
<br />
Here, <math> {z}</math> models the latent variable of the actual patch executed, and <math>\widehat{I_P}</math> belongs to a set of possible neighboring patches <math> \mathcal{P}</math>.<math> P(z=\widehat{I_P}|\theta_D,I_P,\mathcal{R})</math> shows the noise which can be caused by <math>\mathcal{R}</math> variables and is implemented as the Noise Modelling Network (NMN). <math> {P(g│z=\widehat{I_P},\theta_{D}, \mathcal{R} )}</math> shows the grasp prediction probability given the true patch and is implemented as the Grasp Prediction Network (GPN). The overall Robust-Grasp model is computed by marginalizing GPN and NMN.<br />
<br />
===Learning the latent noise model===<br />
<br />
This section concerns what be the inputs to the NMN network should be and how should the inputs can be trained. The authors assume that <math> {z}</math> is conditionally independent of the local patch-specific variables <math> {(I_{P}, \theta_{D})}</math>. To estimate the latent variable <math> {z}</math> given the global information <math>\mathcal{R}</math>, i.e <math> P(z=\widehat{I_P}|\theta_D,I_P,\mathcal{R}) \equiv P(z=\widehat{I_P}|\mathcal{R})</math>. Apart from the patch <math> I_{P} </math> and grasp information <math>(x, y, θ)</math>, they use information like image of the entire scene, ID of the robot and the location of the raw pixel. They argue that the image of the full scene could contain some essential information about the system such as the relative location of camera to the ground which may change over the lifetime of the robot. The identification number of the robot might give cues about errors specific to a particular hardware. Finally, the raw pixels of execution contain calibration specific information, since calibration error is coupled with pixel location, since least squares fit are used to to compute calibration parameters.<br />
<br />
They used direct optimization to learn both NMN and GPN with noisy labels. However, explicit labels are not available to train NMN but the latent variable <math>z</math> can be estimated using a technique such as Expectation-Maximization. The entire image of the scene and the environment information are the inputs of the NMN, as well as robot ID and raw-pixel grasp location. The output of the NMN is the probability distribution of the actual patches where the grasps are executed. Finally, a binary cross entropy loss is applied to the marginalized output of these two networks and the true grasp label <math>g</math>.<br />
<br />
===Training details===<br />
<br />
They implemented their model in PyTorch and fine tuned a pretrained ResNet-18 model. They concatenated 512 dimensional ResNet feature with a 1-hot vector of robot ID and the raw pixel location of the grasp for their NMN. This passes through a series of three fully connected layers and a SoftMax layer to convert the correct patch predictions to a probability distribution. Also, the inputs of the GPN are the original noisy patch plus 8 other equidistant patches from the original one. The angle predictions for all the patches are passed through a sigmoid activation at the end to obtain grasp success probability for a specific patch at a specific angle.<br />
<br />
The training of the network takes place in two stages. It starts with training only GPN over 5 epochs of the data. Then, the NMN and the marginalization operator are added to the model. So, they train NMN and GPN simultaneously in an end-to-end fashion for the other 25 epochs.<br />
<br />
This two-stage approach is crucial for effective training of their networks, without which NMN trivially selects the same patch irrespective of the input. The optimizer used for training is Adam [16].<br />
<br />
==Results==<br />
<br />
In the results part of the paper, they show that collecting dataset in homes is essential for generalizing learning from unseen environments. They also show that modelling the noise in their Low-Cost Arm (LCA) can improve grasping performance.<br />
<br />
They collected data in parallel using multiple robots in 6 different homes, as shown in Figure 3. They used an object detector (tiny-YOLO) as the input data were unstructured due to LCA limited memory and computational capabilities. With an object location detected, class information was discarded, and a grasp was attempted. The grasp location in 3D was computed using PointCloud data. They scattered different objects in homes within 2m area to prevent collision of the robot with obstacles and let the robot move randomly and grasp objects. Finally, they collected a dataset with 28K grasp results.<br />
<br />
[[File:aa3.PNG|600px|thumb|center|]]<br />
<br />
To evaluate their approach in a more quantitative way, they used three test settings:<br />
<br />
- The first one is a binary classification or held-out data. The test set is collected by performing random grasps on objects. They measure the performance of binary classification by predicting the success or failure of grasping, given a location and the angle. Using binary classification allows for testing a lot of models without running them on real robots. They collected two held-out datasets using LCA in lab and homes and the dataset for Baxter robot.<br />
<br />
- The second one is Real Low-Cost Arm (Real-LCA). Here, they evaluate their model by running it in three unseen homes. They put 20 new objects in these three homes in different orientations. Since the objects and the environments are completely new, this tests could measure the generalization of the model.<br />
<br />
- The third one is Real Sawyer (Real-Sawyer). They evaluate the performance of their model by running the model on the Sawyer robot which is more accurate than the LCA. They tested their model in the lab environment to show that training models with the datasets collected from homes can improve the performance of models even in lab environments.<br />
<br />
They used baselines for both their data which is collected in homes and their model which is Robust-Grasp. They used two datasets for the baseline. The dataset collected by (Lab-Baxter) and the dataset collected by their LCA in the lab (Lab-LCA).<br />
They compared their Robust-Grasp model with the noise independent patch grasping model (Patch-Grasp) [4]. They also compared their data and model with DexNet-3.0 (DexNet) for a strong real-world grasping baseline.<br />
<br />
===Experiment 1: Performance on held-out data===<br />
<br />
Table 1 shows that the models trained on lab data cannot generalize to the Home-LCA environment (i.e. they overfit to their respective environments and attain a lower binary classification score). However, the model trained on Home-LCA has a good performance on both lab data and home environment.<br />
<br />
[[File:aa4.PNG|600px|thumb|center|]]<br />
<br />
===Experiment 2: Performance on Real LCA Robot===<br />
<br />
In table 2, the performance of the Home-LCA is compared against a pre-trained DexNet and the model trained on the Lab-Baxter. Training on the Home-LCA dataset performs 43.7% better than training on the Lab-Baxter dataset and 33% better than DexNet. The low performance of DexNet can be described by the possible noise in the depth images that are caused by the natural light. DexNet, which requires high-quality depth sensing, cannot perform well in these scenarios. By using cheap commodity RGBD cameras in LCA, the noise in the depth images is not a matter of concern, as the model has no expectation of high-quality sensing.<br />
<br />
[[File:aa5.PNG|600px|thumb|center|]]<br />
<br />
===Performance on Real Sawyer===<br />
<br />
To compare the performance of the Robust-Grasp model against the Patch-Grasp model without collecting noise-free data, they used Lab-Baxter for benchmarking, which is an accurate and better calibrated robot. The Sawyer robot is used for testing to ensure that the testing robot is different from both training robots. As shown in Table 3, the Robust-Grasp model trained on Home-LCA outperforms the Patch-Grasp model and achieves 77.5% accuracy. This accuracy is similar to several recent papers, however, this model was trained and tested in a different environment. The Robust-Grasp model also outperforms the Patch-Grasp by about 4% on binary classification. Furthermore, the visualizations of predicted noise corrections in Figure 4 shows that the corrections depend on both the pixel locations of the noisy grasp and the robot.<br />
<br />
[[File:aa6.PNG|600px|thumb|center|]]<br />
<br />
[[File:aa7.PNG|600px|thumb|center|]]<br />
<br />
==Related work==<br />
<br />
Over the last few years, the interest of scaling up robot learning with large-scale datasets has been increased. Hence, many papers were published in this area. A hand annotated grasping dataset, a self-supervised grasping dataset, and grasping using reinforcement learning are some examples of using large-scale datasets for grasping. The work mentioned above used high-cost hardware and data labeling mechanisms. There were also many papers that worked on other robotic tasks like material recognition, pushing objects and manipulating a rope. However, none of these papers worked on real data in real environments like homes, they all used lab data.<br />
<br />
Furthermore, since grasping is one of the basic problems in robotics, there were some efforts to improve grasping. Classical approaches focused on physics-based issues of grasping and required 3D models of the objects. However, recent works focused on data-driven approaches which learn from visual observations to grasp objects. Simulation and real-world robots are both required for large-scale data collection. A versatile grasping model was proposed to achieve a 90% performance for a bin-picking task. The point here is that they usually require high-quality depth as input which seems to be a barrier for practical use of robots in real environments. High-quality depth sensing means a high cost to implement in hardware and thus is a barrier for practical use.<br />
<br />
Most labs use industrial robots or standard collaborative hardware for their experiments. Therefore, there is few research that used low-cost robots. One of the examples is learning using a cheap inaccurate robot for stack multiple blocks. Although mobile robots like iRobot’s Roomba have been in the home consumer electronics market for a decade, it is not clear whether learning approaches are used in it alongside mapping and planning.<br />
<br />
Learning from noisy inputs is another challenge specifically in computer vision. A controversial question which is often raised in this area is whether learning from noise can improve the performance. Some works show it could have bad effects on the performance; however, some other works find it valuable when the noise is independent or statistically dependent on the environment. In this paper, they used a model that can exploit the noise and learn a better grasping model.<br />
<br />
==Conclusion==<br />
<br />
All in all, the paper presents an approach for collecting large-scale robot data in real home environments. They implemented their approach by using a mobile manipulator which is a lot cheaper than the existing industrial robots and costs under 3K USD. They collected a dataset of 28K grasps in six different homes. In order to solve the problem of noisy labels which were caused by their inaccurate robots, they presented a framework to factor out the noise in the data. They tested their model by physically grasping 20 new objects in three new homes and in the lab. The model trained with home dataset showed 43.7% improvement over the models trained with lab data. Their framework performed 33% better than a baseline DexNet model, which struggled with the typically poor depth sensing in common household environments with a lot of natural light. Their results also showed that their model can improve the grasping performance even in lab environments. They also demonstrated that their architecture for modeling the noise improved the performance by about 10%.<br />
<br />
==Critiques==<br />
<br />
This paper does not contain a significant algorithmic contribution. They are just combining a large number of data engineering techniques for the robot learning problem. The authors claim that they have obtained 43.7% more accuracy than baseline models, but it does not seem to be a fair comparison as the data collection happened in simulated settings in the lab for other methods, whereas the authors use the home dataset. The authors must have also discussed safety issues when training robots in real environments as against simulated environments like labs. The authors are encouraging other researchers to look outside the labs, but are not discussing the critical safety issues in this approach.<br />
<br />
Another strange finding is that the paper mentions that they "follow a model architecture similar to [Pinto and Gupta [4]]," however, the proposed model is, in fact, a fine-tuned resnet-18 architecture. Pinto and Gupta, implement a version similar to AlexNet as shown below in Figure 5.<br />
<br />
[[File:Figure_5_PandG.JPG | 450px|thumb|center|Figure 5: AlexNet architecture implemented in Pinto and Gupta [4].]]<br />
<br />
<br />
The paper argues that the dataset collected by the LCA is noisy, since the robot is cheap and inaccurate. It further asserts that in order to handle the noise in the dataset, they can model the noise as a latent variable and their model can improve the performance of grasping. Although learning from noisy data and achieving a good performance is valuable, it is better that they test their noise modeling network for other robots as well. Since their noise modelling network takes robot information as an input, it would be a good idea to generalize it by testing it using different inaccurate robots to ensure that it would perform well.<br />
<br />
They did not mention other aspects of their comparison, for example they could mention their training time compared to other models or the size of other datasets.<br />
<br />
==References==<br />
<br />
#Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel. "Domain randomization for transferring deep neural networks from simulation to the real world." 2017. URL https://arxiv.org/abs/1703.06907.<br />
#Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel. "Sim-to-real transfer of robotic control with dynamics randomization." arXiv preprint arXiv:1710.06537,2017.<br />
#Lerrel Pinto, Marcin Andrychowicz, Peter Welinder, Wojciech Zaremba, and Pieter Abbeel. "Asymmetric actor-critic for image-based robot learning." Robotics Science and Systems, 2018.<br />
#Lerrel Pinto and Abhinav Gupta. "Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours." CoRR, abs/1509.06825, 2015. URL http://arxiv.org/abs/1509. 06825.<br />
#Adithyavairavan Murali, Lerrel Pinto, Dhiraj Gandhi, and Abhinav Gupta. "CASSL: Curriculum accelerated self-supervised learning." International Conference on Robotics and Automation, 2018.<br />
# Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel. "End-to-end training of deep visuomotor policies." The Journal of Machine Learning Research, 17(1):1334–1373, 2016.<br />
#Sergey Levine, Peter Pastor, Alex Krizhevsky, and Deirdre Quillen. "Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection." CoRR, abs/1603.02199, 2016. URL http://arxiv.org/abs/1603.02199.<br />
#Pulkit Agarwal, Ashwin Nair, Pieter Abbeel, Jitendra Malik, and Sergey Levine. "Learning to poke by poking: Experiential learning of intuitive physics." 2016. URL http://arxiv.org/ abs/1606.07419<br />
#Chelsea Finn, Ian Goodfellow, and Sergey Levine. "Unsupervised learning for physical interaction through video prediction." In Advances in neural information processing systems, 2016.<br />
#Ashvin Nair, Dian Chen, Pulkit Agrawal, Phillip Isola, Pieter Abbeel, Jitendra Malik, and Sergey Levine. "Combining self-supervised learning and imitation for vision-based rope manipulation." International Conference on Robotics and Automation, 2017.<br />
#Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta. "Revisiting unreasonable effectiveness of data in deep learning era." ICCV, 2017.<br />
#Marc Peter Deisenroth, Carl Edward Rasmussen, and Dieter Fox. Learning to control a low-cost manipulator using data-efficient reinforcement learning. RSS, 2011.<br />
#David F Nettleton, Albert Orriols-Puig, and Albert Fornells. A study of the effect of different types of noise on the precision of supervised learning techniques. Artificial intelligence review, 33(4):275–306, 2010.<br />
#Benoît Frénay and Michel Verleysen. Classification in the presence of label noise: a survey. IEEE transactions on neural networks and learning systems, 25(5):845–869, 2014.<br />
#Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang. Learning from massive noisy labeled data for image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2691–2699, 2015.<br />
#Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Countering_Adversarial_Images_Using_Input_Transformations&diff=42332Countering Adversarial Images Using Input Transformations2018-12-08T01:18:12Z<p>Aghabuss: /* Introduction */</p>
<hr />
<div>The code for this paper is available here[https://github.com/facebookresearch/adversarial_image_defenses]<br />
<br />
==Motivation ==<br />
As the use of machine intelligence has increased, robustness has become a critical feature to guarantee the reliability of deployed machine-learning systems. However, recent research has shown that existing models are not robust to small, adversarially designed perturbations to the input. Adversarial examples are inputs to Machine Learning models so that an attacker has intentionally designed to cause the model to make a mistake. Adversarially perturbed examples have been deployed to attack image classification services (Liu et al., 2016)[11], speech recognition systems (Cisse et al., 2017a)[12], and robot vision (Melis et al., 2017)[13]. The existence of these adversarial examples has motivated proposals for approaches that increase the robustness of learning systems to such examples. In the example below (Goodfellow et. al) [17], a small perturbation is applied to the original image of a panda, changing the prediction to a gibbon.<br />
<br />
[[File:Panda.png|center]]<br />
<br />
==Introduction==<br />
The paper studies strategies that defend against adversarial example attacks on image classification systems by transforming the images before feeding them to a Convolutional Network Classifier. <br />
Generally, defenses against adversarial examples fall into two main categories:<br />
<br />
# Model-Specific – They enforce model properties such as smoothness and invariance via the learning algorithm. <br />
# Model-Agnostic – They try to remove adversarial perturbations from the input. <br />
<br />
Model-specific defense strategies make strong assumptions about expected adversarial attacks. As a result, they violate the Kerckhoffs principle, which states that adversaries can circumvent model-specific defenses by simply changing how an attack is executed. This paper focuses on increasing the effectiveness of model-agnostic defense strategies. Specifically, they investigated the following image transformations as a means for protecting against adversarial images:<br />
<br />
# Image Cropping and Re-scaling (Graese et al, 2016). <br />
# Bit Depth Reduction (Xu et al, 2017) <br />
# JPEG Compression (Dziugaite et al, 2016) <br />
# Total Variance Minimization (Rudin et al, 1992) <br />
# Image Quilting (Efros & Freeman, 2001). <br />
<br />
These image transformations have been studied against Adversarial attacks such as the fast gradient sign method (Goodfelow et. al., 2015), its iterative extension (Kurakin et al., 2016a), Deepfool (Moosavi-Dezfooli et al., 2016), and the Carlini & Wagner (2017) <math>L_2</math>attack. <br />
<br />
The authors in this paper try to focus on increasing the effectiveness of model-agnostic defense strategies through approaches that:<br />
# remove the adversarial perturbations from input images,<br />
# maintain sufficient information in input images to correctly classify them,<br />
# and are still effective in situations where the adversary has information about the defense strategy being used.<br />
<br />
From their experiments, the strongest defenses are based on Total Variance Minimization and Image Quilting. These defenses are non-differentiable and inherently random which makes it difficult for an adversary to get around them. The authors best defenses eliminate 60% of gray-box attacks and 90% of black-box attacks by four major attack methods that perturb pixel values by 8% on average.<br />
<br />
==Previous Work==<br />
Recently, a lot of research has focused on countering adversarial threats. Wang et al [4], proposed a new adversary resistant technique that obstructs attackers from constructing impactful adversarial images. This is done by randomly nullifying features within images. Tramer et al [2], showed the state-of-the-art Ensemble Adversarial Training Method, which augments the training process but not only included adversarial images constructed from their model but also including adversarial images generated from an ensemble of other models. Their method implemented on an Inception V2 classifier finished 1st among 70 submissions of NIPS 2017 competition on Defenses against Adversarial Attacks. Graese, et al. [3], showed how input transformation such as shifting, blurring and noise can render the majority of the adversarial examples as non-adversarial. Xu et al.[5] demonstrated, how feature squeezing methods, such as reducing the color bit depth of each pixel and spatial smoothing, defends against attacks. Dziugaite et al [6], studied the effect of JPG compression on adversarial images. Chen et al. [7] introduce an advanced denoising algorithm with GAN based noise modeling in order to improve the blind denoising performance in low-level vision processing. The GAN is trained to estimate the noise distribution over the input noisy images and to generate noise samples. Although meant for image processing, this method can be generalized to target adversarial examples where the unknown noise generating algorithm can be leveraged.<br />
<br />
==Terminology==<br />
<br />
'''Gray Box Attack''' : Model Architecture and parameters are public.<br />
<br />
'''Black Box Attack''': Consider a weak adversary with access to the DNN output only. The adversary has no knowledge<br />
of the architectural choices made to design the DNN, which include the number, type, and size of layers, nor of<br />
the training data used to learn the DNN’s parameters. Such attacks are referred to as black box, where adversaries need<br />
not know internal details of a system to compromise it [18].<br />
<br />
An interesting and important observation of adversarial examples is that they generally are not model or architecture specific. Adversarial examples generated for one neural network architecture will transfer very well to another architecture. In other words, if you wanted to trick a model you could create your own model and adversarial examples based off of it. Then these same adversarial examples will most probably trick the other model as well. This has huge implications as it means that it is possible to create adversarial examples for a completely black box model where we have no prior knowledge of the internal mechanics. [https://ml.berkeley.edu/blog/2018/01/10/adversarial-examples/ reference]<br />
<br />
'''Non Targeted Adversarial Attack''': The goal of the attack is to modify a source image in a way such that the image will be classified incorrectly by the network.<br />
<br />
This is an example on non-targeted adversarial attacks to be more clear [https://ml.berkeley.edu/blog/2018/01/10/adversarial-examples/ reference]:<br />
[[File:non-targeted O.JPG| 600px|center]]<br />
<br />
'''Targeted Adversarial Attack''': The goal of the attack is to modify a source image in way such that image will be classified as a ''target'' class by the network.<br />
<br />
This is an example on targeted adversarial attacks to be more clear [https://ml.berkeley.edu/blog/2018/01/10/adversarial-examples/ reference]:<br />
[[File:Targeted O.JPG| 600px|center]]<br />
<br />
'''Defense''': A defense is a strategy that aims to make the prediction on an adversarial example <math> h(x') </math> equal to the prediction on the corresponding clean example <math> h(x) </math>.<br />
<br />
== Problem Definition ==<br />
The paper discusses non-targeted adversarial attacks for image recognition systems. Given image space <math>\mathcal{X} = [0,1]^{H \times W \times C}</math>, a source image <math>x \in \mathcal{X}</math>, and a classifier <math>h(.)</math>, a non-targeted adversarial example of <math>x</math> is a perturbed image <math>x'</math>, such that <math>h(x) \neq h(x')</math> and <math>d(x, x') \leq \rho</math> for some dissimilarity function <math>d(·, ·)</math> and <math>\rho \geq 0</math>. In the best case scenario, <math>d(·, ·)</math> measures the perceptual difference between the original image <math>x</math> and the perturbed image <math>x'</math>, but usually, Euclidean distance (<math>||x - x'||_2</math>) or the Chebyshov distance (<math>||x - x'||_{\infty}</math>) are used.<br />
<br />
From a set of N clean images <math>[{x_{1}, …, x_{N}}]</math>, an adversarial attack aims to generate <math>[{x'_{1}, …, x'_{N}}]</math> images, such that (<math>x'_{n}</math>) is an adversary of (<math>x_{n}</math>).<br />
<br />
The success rate of an attack is given as: <br />
<br />
<center><math><br />
\frac{1}{N}\sum_{n=1}^{N}I[h(x_n) &ne; h({x_n}^\prime)],<br />
</math></center><br />
<br />
which is the proportions of predictions that were altered by an attack.<br />
<br />
The success rate is generally measured as a function of the magnitude of perturbations performed by the attack. In this paper, L2 perturbations are used and are quantified using the normalized L2-dissimilarity metric:<br />
<math> \frac{1}{N} \sum_{n=1}^N{\frac{\vert \vert x_n - x'_n \vert \vert_2}{\vert \vert x_n \vert \vert_2}} </math><br />
<br />
A strong adversarial attack has a high rate, while its normalized L2-dissimilarity given by the above equation is less.<br />
<br />
In most practical settings, an adversary does not have direct access to the model <math>h(·)</math> and has to do a black-box attack. <br />
<br />
However, prior work has shown successful attacks by transferring adversarial examples generated for a separately-trained model to an unknown target model (Liu et al., 2016), thus allowing efficient black-box attack. <br />
<br />
As a result, the authors investigate both the black-box and a more difficult gray-box attack setting: the adversary has access to the model architecture and the model parameters, but<br />
is unaware of the defence strategy that is being used.<br />
<br />
A defence is an approach that aims make the prediction on an adversarial example <math>h(x')</math> equal to the prediction on the corresponding clean example <math>h(x)</math>. In this study, the authors focus on image transformation defenses <math>g(x)</math> that perform prediction via <math>h(g(x'))</math>. Ideally, <math>g(·)</math> is a complex, non-differentiable, and potentially stochastic function: this makes it difficult for an adversary to attack the prediction model <math>h(g(x))</math> even when the adversary knows both <math>h(·)</math> and <math>g(·)</math>.<br />
<br />
==Adversarial Attacks==<br />
<br />
Although the exact effect that adversarial examples have on the network is unknown, Ian Goodfellow et. al's Deep Learning book states that adversarial examples exploit the linearity of neural networks to perturb the cost function to force incorrect classifications. Images are often high resolution, and thus have thousands of pixels (millions for HD images). An epsilon ball perturbation when dimensionality is in the magnitude of thousands/millions greatly effects the cost function (especially if it increases loss at every pixel). Hence, although the following methods such as FGSM and Iterative FGSM are very straightforward, they greatly influence the network under a white box attack. <br />
<br />
For the experimental purposes, below 4 attacks have been studied in the paper:<br />
<br />
1. '''Fast Gradient Sign Method (FGSM; Goodfellow et al. (2015)) [17]''': Given a source input <math>x</math>, and true label <math>y</math>, and let <math>l(.,.)</math> be the differentiable loss function used to train the classifier <math>h(.)</math>. Then the corresponding adversarial example is given by:<br />
<br />
<math>x' = x + \epsilon \cdot sign(\nabla_x l(x, y))</math><br />
<br />
for some <math>\epsilon \gt 0</math> which controls the perturbation magnitude.<br />
<br />
2. '''Iterative FGSM ((I-FGSM; Kurakin et al. (2016b)) [14]''': iteratively applies the FGSM update, where M is the number of iterations. It is given as:<br />
<br />
<math>x^{(m)} = x^{(m-1)} + \epsilon \cdot sign(\nabla_{x^{m-1}} l(x^{m-1}, y))</math><br />
<br />
where <math>m = 1,...,M; x^{(0)} = x;</math> and <math>x' = x^{(M)}</math>. M is set such that <math>h(x) \neq h(x')</math>.<br />
<br />
Both FGSM and I-FGSM work by minimizing the Chebyshev distance between the inputs and the generated adversarial examples.<br />
<br />
3. '''DeepFool ((Moosavi-Dezfooliet al., 2016) [15]''': projects x onto a linearization of the decision boundary defined by binary classifier h(.) for M iterations. This can be particularly effictive when a network uses ReLU activation functions. It is given as:<br />
<br />
[[File:DeepFool.PNG|400px |]]<br />
<br />
4. '''Carlini-Wagner's L2 attack (CW-L2; Carlini & Wagner (2017)) [16]''': propose an optimization-based attack that combines a differentiable surrogate for the model’s classification accuracy with an L2-penalty term which encourages the adversary image to be close to the original image. Let <math>Z(x)</math> be the operation that computes the logit vector (i.e., the output before the softmax layer) for an input <math>x</math>, and <math>Z(x)_k</math> be the logit value corresponding to class <math>k</math>. The untargeted variant<br />
of CW-L2 finds a solution to the unconstrained optimization problem. It is given as:<br />
<br />
[[File:Carlini.PNG|500px |]]<br />
<br />
As mentioned earlier, the first two attacks minimize the Chebyshev distance whereas the last two attacks minimize the Euclidean distance between the inputs and the adversarial examples.<br />
<br />
All the methods described above maintain <math>x' \in \mathcal{X}</math> by performing value clipping. <br />
<br />
Below figure shows adversarial images and corresponding perturbations at five levels of normalized L2-dissimilarity for all four attacks, mentioned above.<br />
<br />
[[File:Strength.PNG|thumb|center| 600px |Figure 1: Adversarial images and corresponding perturbations at five levels of normalized L2- dissimilarity for all four attacks.]]<br />
<br />
==Defenses==<br />
Defense is a strategy that aims to make the prediction on an adversarial example equal to the prediction on the corresponding clean example, and the particular structure of adversarial perturbations <math> x-x' </math> have been shown in Figure 1.<br />
Five image transformations that alter the structure of these perturbations have been studied:<br />
# Image Cropping and Re-scaling, <br />
# Bit Depth Reduction, <br />
# JPEG Compression, <br />
# Total Variance Minimization, <br />
# Image Quilting.<br />
<br />
'''Image cropping and Rescaling''' has the effect of altering the spatial positioning of the adversarial perturbation which is important in making attacks successful. In this study, images are cropped and re-scaled during training time as part of data-augmentation. At test time, the predictions of randomly cropped are averaged.<br />
<br />
'''Bit Depth Reduction (Xu et. al) [5]''' performs a simple type of quantization that can remove small (adversarial) variations in pixel values from an image. Images are reduced to 3 bits in the experiment.<br />
<br />
'''JPEG Compression and Decompression (Dziugaite etal., 2016)''' removes small perturbations by performing simple quantization. The authors use a quality level of 75/100 in their experiments<br />
<br />
'''Total Variance Minimization (Rudin et. al) [9]''' :<br />
This combines pixel dropout with total variance minimization. This approach randomly selects a small set of pixels, and reconstructs the “simplest” image that is consistent with the selected pixels. The reconstructed image does not contain the adversarial perturbations because these perturbations tend to be small and localized.Specifically, we first select a random set of pixels by sampling a Bernoulli random variable <math>X(i; j; k)</math> for each pixel location <math>(i; j; k)</math>;we maintain a pixel when <math>(i; j; k)</math>= 1. Next, we use total variation, minimization to constructs an image z that is similar to the (perturbed) input image x for the selected<br />
set of pixels, whilst also being “simple” in terms of total variation by solving:<br />
<br />
[[File:TV!.png|300px|]] , <br />
<br />
where <math>TV_{p}(z)</math> represents <math>L_{p}</math> total variation of '''z''' :<br />
<br />
[[File:TV2.png|500px|]]<br />
<br />
The total variation (TV) measures the amount of fine-scale variation in the image z, as a result of which TV minimization encourages removal of small (adversarial) perturbations in the image. The objective function is convex in <math>z</math>, which makes solving for z straightforward. In the paper, p = 2 and a special-purpose solver based on the split Bregman method (Goldstein & Osher, 2009) to perform total variance minimization efficiently is employed.<br />
The effectiveness of TV minimization is illustrated by the images in the middle column of the figure below: in particular, note that the adversarial perturbations that were present in the background for the non- transformed image (see bottom-left image) have nearly completely disappeared in the TV-minimized adversarial image (bottom-center image). As expected, TV minimization also changes image structure in non-homogeneous regions of the image, but as these perturbations were not adversarially designed we expect the negative effect of these changes to be limited.<br />
<br />
[[File:tvx.png]]<br />
<br />
The figure above represents an illustration of total variance minimization and image quilting applied to an original and an adversarial image (produced using I-FGSM with ε = 0.03, corresponding to a normalized L2 - dissimilarity of 0.075). From left to right, the columns correspond to (1) no transformation, (2) total variance minimization, and (3) image quilting. From top to bottom, rows correspond to: (1) the original image, (2) the corresponding adversarial image produced by I-FGSM, and (3) the absolute difference between the two images above. Difference images were multiplied by a constant scaling factor to increase visibility.<br />
<br />
<br />
'''Image Quilting (Efros & Freeman, 2001) [8]'''<br />
Image Quilting is a non-parametric technique that synthesizes images by piecing together small patches that are taken from a database of image patches. The algorithm places appropriate patches in the database for a predefined set of grid points and computes minimum graph cuts in all overlapping boundary regions to remove edge artifacts. Image Quilting can be used to remove adversarial perturbations by constructing a patch database that only contains patches from "clean" images ( without adversarial perturbations); the patches used to create the synthesized image are selected by finding the K nearest neighbors ( in pixel space) of the corresponding patch from the adversarial image in the patch database, and picking one of these neighbors uniformly at random. The motivation for this defense is that resulting image only contains pixels that were not modified by the adversary - the database of real patches is unlikely to contain the structures that appear in adversarial images.<br />
<br />
If we take a look at the effect of image quilting in the above figure, although interpretation of these images is more complicated due to the quantization errors that image quilting introduces, we can still observe that the absolute differences between quilted original and the quilted adversarial image appear to be smaller in non-homogeneous regions of the image. Based on this observation the authors suggest that TV minimization and image quilting lead to inherently different defenses.<br />
<br />
=Experiments=<br />
<br />
Five experiments were performed to test the efficacy of defenses. The first four experiments consider gray and black box attacks. The gray-box attack applies defenses on input adversarial images for the convolutional networks. The adversary is able to read model architecture and parameters but not the defense strategy. The black-box attack replaces convolutional network by a trained network with image-transformations. The final experiment compares the authors' defenses with prior work. <br />
<br />
'''Set up:'''<br />
Experiments are performed on the ImageNet image classification dataset. The dataset comprises 1.2 million training images and 50,000 test images that correspond to one of 1000 classes. The adversarial images are produced by attacking a ResNet-50 model, with different kinds of attacks mentioned in Section5. The strength of an adversary is measured in terms of its normalized L2-dissimilarity. To produce the adversarial images, L2 dissimilarity for each of the attack was set as below:<br />
<br />
- FGSM. Increasing the step size <math>\epsilon</math>, increases the normalized L2-dissimilarity.<br />
<br />
- I-FGSM. We fix M=10, and increase <math>\epsilon</math> to increase the normalized L2-dissimilarity.<br />
<br />
- DeepFool. We fix M=5, and increase <math>\epsilon</math> to increase the normalized L2-dissimilarity.<br />
<br />
- CW-L2. We fix <math>k</math>=0 and <math>\lambda_{f}</math> =10, and multiply the resulting perturbation <br />
<br />
The hyperparameters of the defenses have been fixed in all the experiments. Specifically the pixel dropout probability was set to <math>p</math>=0.5 and regularization parameter of total variation minimizer <math>\lambda_{TV}</math>=0.03.<br />
<br />
Below figure shows the difference between the set up in different experiments below. The network is either trained on a) regular images or b) transformed images. The different settings are marked by 8.1, 8.2 and 8.3 <br />
[[File:models3.png |center]] <br />
<br />
==GrayBox - Image Transformation at Test Time== <br />
This experiment applies a transformation on adversarial images at test time before feeding them to a ResNet -50 which was trained to classify clean images. Below figure shows the results for five different transformations applied and their corresponding Top-1 accuracy. Few of the interesting observations from the plot are: All of the image transformations partly eliminate the effects of the attack, Crop ensemble gives the best accuracy around 40-60 percent, with an ensemble size of 30. The accuracy of Image Quilting Defense hardly deteriorates as the strength of the adversary increases. However, it does impact accuracy on non-adversarial examples.<br />
<br />
[[File:sFig4.png|center|600px |]]<br />
<br />
==BlackBox - Image Transformation at Training and Test Time==<br />
ResNet-50 model was trained on transformed ImageNet Training images. Before feeding the images to the network for training, standard data augmentation (from He et al) along with bit depth reduction, JPEG Compression, TV Minimization, or Image Quilting were applied on the images. The classification accuracy on the same adversarial images as in the previous case is shown Figure below. (Adversary cannot get this trained model to generate new images - Hence this is assumed as a Black Box setting!). Below figure concludes that training Convolutional Neural Networks on images that are transformed in the same way at test time, dramatically improves the effectiveness of all transformation defenses. Nearly 80 -90 % of the attacks are defended successfully, even when the L2- dissimilarity is high.<br />
<br />
<br />
[[File:sFig5.png|center|600px |]]<br />
<br />
<br />
==Blackbox - Ensembling==<br />
Four networks ResNet-50, ResNet-10, DenseNet-169, and Inception-v4 along with an ensemble of defenses were studied, as shown in Table 1. The adversarial images are produced by attacking a ResNet-50 model. The results in the table conclude that Inception-v4 performs best. This could be due to that network having a higher accuracy even in non-adversarial settings. The best ensemble of defenses achieves an accuracy of about 71% against all the other attacks. The attacks deteriorate the accuracy of the best defenses (a combination of cropping, TVM, image quilting, and model transfer) by at most 6%. Gains of 1-2% in classification accuracy could be found from ensembling different defenses, while gains of 2-3% were found from transferring attacks to different network architectures.<br />
<br />
<br />
[[File:sTab1.png|600px|thumb|center|Table 1. Top-1 classification accuracy of ensemble and model transfer defenses (columns) against four black-box attacks (rows). The four networks we use to classify images are ResNet-50 (RN50), ResNet-101 (RN101), DenseNet-169 (DN169), and Inception-v4 (Iv4). Adversarial images are generated by running attacks against the ResNet-50 model, aiming for an average normalized <math>L_2</math>-dissimilarity of 0.06. Higher is better. The best defense against each attack is typeset in boldface.]]<br />
<br />
==GrayBox - Image Transformation at Training and Test Time ==<br />
In this experiment, the adversary has access to the network and the related parameters (but does not have access to the input transformations applied at test time). From the network trained in-(BlackBox: Image Transformation at Training and Test Time), novel adversarial images were generated by the four attack methods. The results show that Bit-Depth Reduction and JPEG Compression are weak defenses in such a gray box setting. In contrast, image cropping, rescaling, variation minimization, and image quilting are more robust against adversarial images in this setting.<br />
The results for this experiment are shown in below figure. Networks using these defenses classify up to 50 % of images correctly.<br />
<br />
[[File:sFig6.png|center| 600px |]]<br />
<br />
==Comparison With Ensemble Adversarial Training==<br />
The results of the experiment are compared with the state of the art ensemble adversarial training approach proposed by Tramer et al. [2]. Ensemble Training fits the parameters of a Convolutional Neural Network on adversarial examples that were generated to attack an ensemble of pre-trained models. The model release by Tramer et al [2]: an Inception-Resnet-v2, trained on adversarial examples generated by FGSM against Inception-Resnet-v2 and Inception-v3 models. The authors compared their ResNet-50 models with image cropping, total variance minimization and image quilting defenses. Two assumption differences need to be noticed. Their defenses assume the input transformation is unknown to the adversary and no prior knowledge of the attacks is being used. The results of ensemble training and the pre-processing techniques mentioned in this paper are shown in Table 2. The results show that ensemble adversarial training works better on FGSM attacks (which it uses at training time), but is outperformed by each of the transformation-based defenses all other attacks.<br />
<br />
<br />
<br />
[[File:sTab2.png|600px|thumb|center|Table 2. Top-1 classification accuracy on images perturbed using attacks against ResNet-50 models trained on input-transformed images and an Inception-v4 model trained using ensemble adversarial. Adversarial images are generated by running attacks against the models, aiming for an average normalized <math>L_2</math>-dissimilarity of 0.06. The best defense against each attack is typeset in boldface.]]<br />
<br />
=Discussion/Conclusions=<br />
The paper proposed reasonable approaches to countering adversarial images. The authors evaluated Total Variance Minimization and Image Quilting and compared it with already proposed ideas like Image Cropping - Rescaling, Bit Depth Reduction, JPEG Compression, and Decompression on the challenging ImageNet dataset.<br />
Previous work by Wang et al. [10] shows that a strong input defense should be nondifferentiable and randomized. Two of the defenses - namely Total Variation Minimization and Image Quilting, both possess this property. However, it may still be possible to train a network to perhaps act as an approximation to the non-differentiable transformation. <br />
<br />
Image quilting involves a discrete variable that conducts the selection of a patch from the database, which is a non-differentiable operation.<br />
Additionally, total variation minimization randomly conducts pixels selection from the pixels it uses to measure reconstruction<br />
error during creation of the de-noised image. Image quilting conducts a random selection of a particular K<br />
nearest neighbor uniformly but in a random manner. This inherent randomness makes it difficult to attack the model. <br />
<br />
Future work suggests applying the same techniques to other domains such as speech recognition and image segmentation. For example, in speech recognition, total variance minimization can be used to remove perturbations from waveforms and "spectrogram quilting" techniques that reconstruct a spectrogram could be developed. The proposed input-transformation defenses can also be combined with ensemble adversarial training by Tramèr et al.[2] to study new attack methods.<br />
<br />
=Critiques=<br />
1. The terminology of Black Box, White Box, and Grey Box attack is not exactly given and clear.<br />
<br />
2. White Box attacks could have been considered where the adversary has a full access to the model as well as the pre-processing techniques.<br />
<br />
3. Though the authors did a considerable work in showing the effect of four attacks on ImageNet database, much stronger attacks (Madry et al) [7], could have been evaluated.<br />
<br />
4. Authors claim that the success rate is generally measured as a function of the magnitude of perturbations, performed by the attack using the L2- dissimilarity, but the claim is not supported by any references. None of the previous work has used these metrics.<br />
<br />
5. ([https://openreview.net/forum?id=SyJ7ClWCb])In the new draft of the paper, the authors add the sentence "our defenses assume that part of the defense strategy (viz., the input transformation) is unknown to the adversary".<br />
<br />
This is a completely unreasonable assumption. Any algorithm which hopes to be secure must allow the adversary to, at the very least, understand what the defense is that's being used. Consider a world where the defense here is implemented in practice: any attacker in the world could just go look up the paper, read the description of the algorithm, and know how it works.<br />
<br />
=References=<br />
<br />
1. Chuan Guo , Mayank Rana & Moustapha Ciss´e & Laurens van der Maaten , Countering Adversarial Images Using Input Transformations<br />
<br />
2. Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, Patrick McDaniel, Ensemble Adversarial Training: Attacks and defenses.<br />
<br />
3. Abigail Graese, Andras Rozsa, and Terrance E. Boult. Assessing threat of adversarial examples of deep neural networks. CoRR, abs/1610.04256, 2016. <br />
<br />
4. Qinglong Wang, Wenbo Guo, Kaixuan Zhang, Alexander G. Ororbia II, Xinyu Xing, C. Lee Giles, and Xue Liu. Adversary resistant deep neural networks with an application to malware detection. CoRR, abs/1610.01239, 2016a.<br />
<br />
5. Weilin Xu, David Evans, and Yanjun Qi. Feature squeezing: Detecting adversarial examples in deep neural networks. CoRR, abs/1704.01155, 2017. <br />
<br />
6. Gintare Karolina Dziugaite, Zoubin Ghahramani, and Daniel Roy. A study of the effect of JPG compression on adversarial images. CoRR, abs/1608.00853, 2016.<br />
<br />
7. Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu .Towards Deep Learning Models Resistant to Adversarial Attacks, arXiv:1706.06083v3<br />
<br />
8. Alexei Efros and William Freeman. Image quilting for texture synthesis and transfer. In Proc. SIGGRAPH, pp. 341–346, 2001.<br />
<br />
9. Leonid Rudin, Stanley Osher, and Emad Fatemi. Nonlinear total variation based noise removal algorithms. Physica D, 60:259–268, 1992.<br />
<br />
10. Qinglong Wang, Wenbo Guo, Kaixuan Zhang, Alexander G. Ororbia II, Xinyu Xing, C. Lee Giles, and Xue Liu. Learning adversary-resistant deep neural networks. CoRR, abs/1612.01401, 2016b.<br />
<br />
11. Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song. Delving into transferable adversarial examples and black-box attacks. CoRR, abs/1611.02770, 2016.<br />
<br />
12. Moustapha Cisse, Yossi Adi, Natalia Neverova, and Joseph Keshet. Houdini: Fooling deep structured prediction models. CoRR, abs/1707.05373, 2017 <br />
<br />
13. Marco Melis, Ambra Demontis, Battista Biggio, Gavin Brown, Giorgio Fumera, and Fabio Roli. Is deep learning safe for robot vision? adversarial examples against the icub humanoid. CoRR,abs/1708.06939, 2017.<br />
<br />
14. Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio. Adversarial examples in the physical world. CoRR, abs/1607.02533, 2016b.<br />
<br />
15. Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard. Deepfool: A simple and accurate method to fool deep neural networks. In Proc. CVPR, pp. 2574–2582, 2016.<br />
<br />
16. Nicholas Carlini and David A. Wagner. Towards evaluating the robustness of neural networks. In IEEE Symposium on Security and Privacy, pp. 39–57, 2017.<br />
<br />
17. Ian Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. In Proc. ICLR, 2015.<br />
<br />
18. Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami. Practical black-box attacks against machine learning. In ACM Asia Conference on Computer and Communications Security, 2017.</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Countering_Adversarial_Images_Using_Input_Transformations&diff=42331Countering Adversarial Images Using Input Transformations2018-12-08T01:17:55Z<p>Aghabuss: /* Introduction */</p>
<hr />
<div>The code for this paper is available here[https://github.com/facebookresearch/adversarial_image_defenses]<br />
<br />
==Motivation ==<br />
As the use of machine intelligence has increased, robustness has become a critical feature to guarantee the reliability of deployed machine-learning systems. However, recent research has shown that existing models are not robust to small, adversarially designed perturbations to the input. Adversarial examples are inputs to Machine Learning models so that an attacker has intentionally designed to cause the model to make a mistake. Adversarially perturbed examples have been deployed to attack image classification services (Liu et al., 2016)[11], speech recognition systems (Cisse et al., 2017a)[12], and robot vision (Melis et al., 2017)[13]. The existence of these adversarial examples has motivated proposals for approaches that increase the robustness of learning systems to such examples. In the example below (Goodfellow et. al) [17], a small perturbation is applied to the original image of a panda, changing the prediction to a gibbon.<br />
<br />
[[File:Panda.png|center]]<br />
<br />
==Introduction==<br />
The paper studies strategies that defend against adversarial example attacks on image classification systems by transforming the images before feeding them to a Convolutional Network Classifier. <br />
Generally, defenses against adversarial examples fall into two main categories:<br />
<br />
# Model-Specific – They enforce model properties such as smoothness and invariance via the learning algorithm. <br />
# Model-Agnostic – They try to remove adversarial perturbations from the input. <br />
<br />
Model-specific defense strategies make strong assumptions about expected adversarial attacks. As a result, they violate the Kerckhoffs principle, which states that adversaries can circumvent model-specific defenses by simply changing how an attack is executed. This paper focuses on increasing the effectiveness of model-agnostic defense strategies. Specifically, they investigated the following image transformations as a means for protecting against adversarial images:<br />
<br />
# Image Cropping and Re-scaling (Graese et al, 2016). <br />
# Bit Depth Reduction (Xu et al, 2017) <br />
# JPEG Compression (Dziugaite et al, 2016) <br />
# Total Variance Minimization (Rudin et al, 1992) <br />
# Image Quilting (Efros & Freeman, 2001). <br />
<br />
These image transformations have been studied against Adversarial attacks such as the fast gradient sign method (Goodfelow et. al., 2015), its iterative extension (Kurakin et al., 2016a), Deepfool (Moosavi-Dezfooli et al., 2016), and the Carlini & Wagner (2017) <math>L_2</math>attack. <br />
<br />
The authors in this paper try to focus on increasing the effectiveness of model-agnostic defense strategies through approaches that:<br />
# remove the adversarial perturbations from input images,<br />
# maintain sufficient information in input images to correctly classify them,<br />
# and are still effective in situations where the adversary has information about the defense strategy being used.<br />
<br />
From their experiments, the strongest defenses are based on Total Variance Minimization and Image Quilting. These defenses are non-differentiable and inherently random which makes it difficult for an adversary to get around them. The authors best defenses eliminate 60%of gray-box attacks and 90% of black-box attacks by four major attack methods that perturb pixel values by 8% on average.<br />
<br />
==Previous Work==<br />
Recently, a lot of research has focused on countering adversarial threats. Wang et al [4], proposed a new adversary resistant technique that obstructs attackers from constructing impactful adversarial images. This is done by randomly nullifying features within images. Tramer et al [2], showed the state-of-the-art Ensemble Adversarial Training Method, which augments the training process but not only included adversarial images constructed from their model but also including adversarial images generated from an ensemble of other models. Their method implemented on an Inception V2 classifier finished 1st among 70 submissions of NIPS 2017 competition on Defenses against Adversarial Attacks. Graese, et al. [3], showed how input transformation such as shifting, blurring and noise can render the majority of the adversarial examples as non-adversarial. Xu et al.[5] demonstrated, how feature squeezing methods, such as reducing the color bit depth of each pixel and spatial smoothing, defends against attacks. Dziugaite et al [6], studied the effect of JPG compression on adversarial images. Chen et al. [7] introduce an advanced denoising algorithm with GAN based noise modeling in order to improve the blind denoising performance in low-level vision processing. The GAN is trained to estimate the noise distribution over the input noisy images and to generate noise samples. Although meant for image processing, this method can be generalized to target adversarial examples where the unknown noise generating algorithm can be leveraged.<br />
<br />
==Terminology==<br />
<br />
'''Gray Box Attack''' : Model Architecture and parameters are public.<br />
<br />
'''Black Box Attack''': Consider a weak adversary with access to the DNN output only. The adversary has no knowledge<br />
of the architectural choices made to design the DNN, which include the number, type, and size of layers, nor of<br />
the training data used to learn the DNN’s parameters. Such attacks are referred to as black box, where adversaries need<br />
not know internal details of a system to compromise it [18].<br />
<br />
An interesting and important observation of adversarial examples is that they generally are not model or architecture specific. Adversarial examples generated for one neural network architecture will transfer very well to another architecture. In other words, if you wanted to trick a model you could create your own model and adversarial examples based off of it. Then these same adversarial examples will most probably trick the other model as well. This has huge implications as it means that it is possible to create adversarial examples for a completely black box model where we have no prior knowledge of the internal mechanics. [https://ml.berkeley.edu/blog/2018/01/10/adversarial-examples/ reference]<br />
<br />
'''Non Targeted Adversarial Attack''': The goal of the attack is to modify a source image in a way such that the image will be classified incorrectly by the network.<br />
<br />
This is an example on non-targeted adversarial attacks to be more clear [https://ml.berkeley.edu/blog/2018/01/10/adversarial-examples/ reference]:<br />
[[File:non-targeted O.JPG| 600px|center]]<br />
<br />
'''Targeted Adversarial Attack''': The goal of the attack is to modify a source image in way such that image will be classified as a ''target'' class by the network.<br />
<br />
This is an example on targeted adversarial attacks to be more clear [https://ml.berkeley.edu/blog/2018/01/10/adversarial-examples/ reference]:<br />
[[File:Targeted O.JPG| 600px|center]]<br />
<br />
'''Defense''': A defense is a strategy that aims to make the prediction on an adversarial example <math> h(x') </math> equal to the prediction on the corresponding clean example <math> h(x) </math>.<br />
<br />
== Problem Definition ==<br />
The paper discusses non-targeted adversarial attacks for image recognition systems. Given image space <math>\mathcal{X} = [0,1]^{H \times W \times C}</math>, a source image <math>x \in \mathcal{X}</math>, and a classifier <math>h(.)</math>, a non-targeted adversarial example of <math>x</math> is a perturbed image <math>x'</math>, such that <math>h(x) \neq h(x')</math> and <math>d(x, x') \leq \rho</math> for some dissimilarity function <math>d(·, ·)</math> and <math>\rho \geq 0</math>. In the best case scenario, <math>d(·, ·)</math> measures the perceptual difference between the original image <math>x</math> and the perturbed image <math>x'</math>, but usually, Euclidean distance (<math>||x - x'||_2</math>) or the Chebyshov distance (<math>||x - x'||_{\infty}</math>) are used.<br />
<br />
From a set of N clean images <math>[{x_{1}, …, x_{N}}]</math>, an adversarial attack aims to generate <math>[{x'_{1}, …, x'_{N}}]</math> images, such that (<math>x'_{n}</math>) is an adversary of (<math>x_{n}</math>).<br />
<br />
The success rate of an attack is given as: <br />
<br />
<center><math><br />
\frac{1}{N}\sum_{n=1}^{N}I[h(x_n) &ne; h({x_n}^\prime)],<br />
</math></center><br />
<br />
which is the proportions of predictions that were altered by an attack.<br />
<br />
The success rate is generally measured as a function of the magnitude of perturbations performed by the attack. In this paper, L2 perturbations are used and are quantified using the normalized L2-dissimilarity metric:<br />
<math> \frac{1}{N} \sum_{n=1}^N{\frac{\vert \vert x_n - x'_n \vert \vert_2}{\vert \vert x_n \vert \vert_2}} </math><br />
<br />
A strong adversarial attack has a high rate, while its normalized L2-dissimilarity given by the above equation is less.<br />
<br />
In most practical settings, an adversary does not have direct access to the model <math>h(·)</math> and has to do a black-box attack. <br />
<br />
However, prior work has shown successful attacks by transferring adversarial examples generated for a separately-trained model to an unknown target model (Liu et al., 2016), thus allowing efficient black-box attack. <br />
<br />
As a result, the authors investigate both the black-box and a more difficult gray-box attack setting: the adversary has access to the model architecture and the model parameters, but<br />
is unaware of the defence strategy that is being used.<br />
<br />
A defence is an approach that aims make the prediction on an adversarial example <math>h(x')</math> equal to the prediction on the corresponding clean example <math>h(x)</math>. In this study, the authors focus on image transformation defenses <math>g(x)</math> that perform prediction via <math>h(g(x'))</math>. Ideally, <math>g(·)</math> is a complex, non-differentiable, and potentially stochastic function: this makes it difficult for an adversary to attack the prediction model <math>h(g(x))</math> even when the adversary knows both <math>h(·)</math> and <math>g(·)</math>.<br />
<br />
==Adversarial Attacks==<br />
<br />
Although the exact effect that adversarial examples have on the network is unknown, Ian Goodfellow et. al's Deep Learning book states that adversarial examples exploit the linearity of neural networks to perturb the cost function to force incorrect classifications. Images are often high resolution, and thus have thousands of pixels (millions for HD images). An epsilon ball perturbation when dimensionality is in the magnitude of thousands/millions greatly effects the cost function (especially if it increases loss at every pixel). Hence, although the following methods such as FGSM and Iterative FGSM are very straightforward, they greatly influence the network under a white box attack. <br />
<br />
For the experimental purposes, below 4 attacks have been studied in the paper:<br />
<br />
1. '''Fast Gradient Sign Method (FGSM; Goodfellow et al. (2015)) [17]''': Given a source input <math>x</math>, and true label <math>y</math>, and let <math>l(.,.)</math> be the differentiable loss function used to train the classifier <math>h(.)</math>. Then the corresponding adversarial example is given by:<br />
<br />
<math>x' = x + \epsilon \cdot sign(\nabla_x l(x, y))</math><br />
<br />
for some <math>\epsilon \gt 0</math> which controls the perturbation magnitude.<br />
<br />
2. '''Iterative FGSM ((I-FGSM; Kurakin et al. (2016b)) [14]''': iteratively applies the FGSM update, where M is the number of iterations. It is given as:<br />
<br />
<math>x^{(m)} = x^{(m-1)} + \epsilon \cdot sign(\nabla_{x^{m-1}} l(x^{m-1}, y))</math><br />
<br />
where <math>m = 1,...,M; x^{(0)} = x;</math> and <math>x' = x^{(M)}</math>. M is set such that <math>h(x) \neq h(x')</math>.<br />
<br />
Both FGSM and I-FGSM work by minimizing the Chebyshev distance between the inputs and the generated adversarial examples.<br />
<br />
3. '''DeepFool ((Moosavi-Dezfooliet al., 2016) [15]''': projects x onto a linearization of the decision boundary defined by binary classifier h(.) for M iterations. This can be particularly effictive when a network uses ReLU activation functions. It is given as:<br />
<br />
[[File:DeepFool.PNG|400px |]]<br />
<br />
4. '''Carlini-Wagner's L2 attack (CW-L2; Carlini & Wagner (2017)) [16]''': propose an optimization-based attack that combines a differentiable surrogate for the model’s classification accuracy with an L2-penalty term which encourages the adversary image to be close to the original image. Let <math>Z(x)</math> be the operation that computes the logit vector (i.e., the output before the softmax layer) for an input <math>x</math>, and <math>Z(x)_k</math> be the logit value corresponding to class <math>k</math>. The untargeted variant<br />
of CW-L2 finds a solution to the unconstrained optimization problem. It is given as:<br />
<br />
[[File:Carlini.PNG|500px |]]<br />
<br />
As mentioned earlier, the first two attacks minimize the Chebyshev distance whereas the last two attacks minimize the Euclidean distance between the inputs and the adversarial examples.<br />
<br />
All the methods described above maintain <math>x' \in \mathcal{X}</math> by performing value clipping. <br />
<br />
Below figure shows adversarial images and corresponding perturbations at five levels of normalized L2-dissimilarity for all four attacks, mentioned above.<br />
<br />
[[File:Strength.PNG|thumb|center| 600px |Figure 1: Adversarial images and corresponding perturbations at five levels of normalized L2- dissimilarity for all four attacks.]]<br />
<br />
==Defenses==<br />
Defense is a strategy that aims to make the prediction on an adversarial example equal to the prediction on the corresponding clean example, and the particular structure of adversarial perturbations <math> x-x' </math> have been shown in Figure 1.<br />
Five image transformations that alter the structure of these perturbations have been studied:<br />
# Image Cropping and Re-scaling, <br />
# Bit Depth Reduction, <br />
# JPEG Compression, <br />
# Total Variance Minimization, <br />
# Image Quilting.<br />
<br />
'''Image cropping and Rescaling''' has the effect of altering the spatial positioning of the adversarial perturbation which is important in making attacks successful. In this study, images are cropped and re-scaled during training time as part of data-augmentation. At test time, the predictions of randomly cropped are averaged.<br />
<br />
'''Bit Depth Reduction (Xu et. al) [5]''' performs a simple type of quantization that can remove small (adversarial) variations in pixel values from an image. Images are reduced to 3 bits in the experiment.<br />
<br />
'''JPEG Compression and Decompression (Dziugaite etal., 2016)''' removes small perturbations by performing simple quantization. The authors use a quality level of 75/100 in their experiments<br />
<br />
'''Total Variance Minimization (Rudin et. al) [9]''' :<br />
This combines pixel dropout with total variance minimization. This approach randomly selects a small set of pixels, and reconstructs the “simplest” image that is consistent with the selected pixels. The reconstructed image does not contain the adversarial perturbations because these perturbations tend to be small and localized.Specifically, we first select a random set of pixels by sampling a Bernoulli random variable <math>X(i; j; k)</math> for each pixel location <math>(i; j; k)</math>;we maintain a pixel when <math>(i; j; k)</math>= 1. Next, we use total variation, minimization to constructs an image z that is similar to the (perturbed) input image x for the selected<br />
set of pixels, whilst also being “simple” in terms of total variation by solving:<br />
<br />
[[File:TV!.png|300px|]] , <br />
<br />
where <math>TV_{p}(z)</math> represents <math>L_{p}</math> total variation of '''z''' :<br />
<br />
[[File:TV2.png|500px|]]<br />
<br />
The total variation (TV) measures the amount of fine-scale variation in the image z, as a result of which TV minimization encourages removal of small (adversarial) perturbations in the image. The objective function is convex in <math>z</math>, which makes solving for z straightforward. In the paper, p = 2 and a special-purpose solver based on the split Bregman method (Goldstein & Osher, 2009) to perform total variance minimization efficiently is employed.<br />
The effectiveness of TV minimization is illustrated by the images in the middle column of the figure below: in particular, note that the adversarial perturbations that were present in the background for the non- transformed image (see bottom-left image) have nearly completely disappeared in the TV-minimized adversarial image (bottom-center image). As expected, TV minimization also changes image structure in non-homogeneous regions of the image, but as these perturbations were not adversarially designed we expect the negative effect of these changes to be limited.<br />
<br />
[[File:tvx.png]]<br />
<br />
The figure above represents an illustration of total variance minimization and image quilting applied to an original and an adversarial image (produced using I-FGSM with ε = 0.03, corresponding to a normalized L2 - dissimilarity of 0.075). From left to right, the columns correspond to (1) no transformation, (2) total variance minimization, and (3) image quilting. From top to bottom, rows correspond to: (1) the original image, (2) the corresponding adversarial image produced by I-FGSM, and (3) the absolute difference between the two images above. Difference images were multiplied by a constant scaling factor to increase visibility.<br />
<br />
<br />
'''Image Quilting (Efros & Freeman, 2001) [8]'''<br />
Image Quilting is a non-parametric technique that synthesizes images by piecing together small patches that are taken from a database of image patches. The algorithm places appropriate patches in the database for a predefined set of grid points and computes minimum graph cuts in all overlapping boundary regions to remove edge artifacts. Image Quilting can be used to remove adversarial perturbations by constructing a patch database that only contains patches from "clean" images ( without adversarial perturbations); the patches used to create the synthesized image are selected by finding the K nearest neighbors ( in pixel space) of the corresponding patch from the adversarial image in the patch database, and picking one of these neighbors uniformly at random. The motivation for this defense is that resulting image only contains pixels that were not modified by the adversary - the database of real patches is unlikely to contain the structures that appear in adversarial images.<br />
<br />
If we take a look at the effect of image quilting in the above figure, although interpretation of these images is more complicated due to the quantization errors that image quilting introduces, we can still observe that the absolute differences between quilted original and the quilted adversarial image appear to be smaller in non-homogeneous regions of the image. Based on this observation the authors suggest that TV minimization and image quilting lead to inherently different defenses.<br />
<br />
=Experiments=<br />
<br />
Five experiments were performed to test the efficacy of defenses. The first four experiments consider gray and black box attacks. The gray-box attack applies defenses on input adversarial images for the convolutional networks. The adversary is able to read model architecture and parameters but not the defense strategy. The black-box attack replaces convolutional network by a trained network with image-transformations. The final experiment compares the authors' defenses with prior work. <br />
<br />
'''Set up:'''<br />
Experiments are performed on the ImageNet image classification dataset. The dataset comprises 1.2 million training images and 50,000 test images that correspond to one of 1000 classes. The adversarial images are produced by attacking a ResNet-50 model, with different kinds of attacks mentioned in Section5. The strength of an adversary is measured in terms of its normalized L2-dissimilarity. To produce the adversarial images, L2 dissimilarity for each of the attack was set as below:<br />
<br />
- FGSM. Increasing the step size <math>\epsilon</math>, increases the normalized L2-dissimilarity.<br />
<br />
- I-FGSM. We fix M=10, and increase <math>\epsilon</math> to increase the normalized L2-dissimilarity.<br />
<br />
- DeepFool. We fix M=5, and increase <math>\epsilon</math> to increase the normalized L2-dissimilarity.<br />
<br />
- CW-L2. We fix <math>k</math>=0 and <math>\lambda_{f}</math> =10, and multiply the resulting perturbation <br />
<br />
The hyperparameters of the defenses have been fixed in all the experiments. Specifically the pixel dropout probability was set to <math>p</math>=0.5 and regularization parameter of total variation minimizer <math>\lambda_{TV}</math>=0.03.<br />
<br />
Below figure shows the difference between the set up in different experiments below. The network is either trained on a) regular images or b) transformed images. The different settings are marked by 8.1, 8.2 and 8.3 <br />
[[File:models3.png |center]] <br />
<br />
==GrayBox - Image Transformation at Test Time== <br />
This experiment applies a transformation on adversarial images at test time before feeding them to a ResNet -50 which was trained to classify clean images. Below figure shows the results for five different transformations applied and their corresponding Top-1 accuracy. Few of the interesting observations from the plot are: All of the image transformations partly eliminate the effects of the attack, Crop ensemble gives the best accuracy around 40-60 percent, with an ensemble size of 30. The accuracy of Image Quilting Defense hardly deteriorates as the strength of the adversary increases. However, it does impact accuracy on non-adversarial examples.<br />
<br />
[[File:sFig4.png|center|600px |]]<br />
<br />
==BlackBox - Image Transformation at Training and Test Time==<br />
ResNet-50 model was trained on transformed ImageNet Training images. Before feeding the images to the network for training, standard data augmentation (from He et al) along with bit depth reduction, JPEG Compression, TV Minimization, or Image Quilting were applied on the images. The classification accuracy on the same adversarial images as in the previous case is shown Figure below. (Adversary cannot get this trained model to generate new images - Hence this is assumed as a Black Box setting!). Below figure concludes that training Convolutional Neural Networks on images that are transformed in the same way at test time, dramatically improves the effectiveness of all transformation defenses. Nearly 80 -90 % of the attacks are defended successfully, even when the L2- dissimilarity is high.<br />
<br />
<br />
[[File:sFig5.png|center|600px |]]<br />
<br />
<br />
==Blackbox - Ensembling==<br />
Four networks ResNet-50, ResNet-10, DenseNet-169, and Inception-v4 along with an ensemble of defenses were studied, as shown in Table 1. The adversarial images are produced by attacking a ResNet-50 model. The results in the table conclude that Inception-v4 performs best. This could be due to that network having a higher accuracy even in non-adversarial settings. The best ensemble of defenses achieves an accuracy of about 71% against all the other attacks. The attacks deteriorate the accuracy of the best defenses (a combination of cropping, TVM, image quilting, and model transfer) by at most 6%. Gains of 1-2% in classification accuracy could be found from ensembling different defenses, while gains of 2-3% were found from transferring attacks to different network architectures.<br />
<br />
<br />
[[File:sTab1.png|600px|thumb|center|Table 1. Top-1 classification accuracy of ensemble and model transfer defenses (columns) against four black-box attacks (rows). The four networks we use to classify images are ResNet-50 (RN50), ResNet-101 (RN101), DenseNet-169 (DN169), and Inception-v4 (Iv4). Adversarial images are generated by running attacks against the ResNet-50 model, aiming for an average normalized <math>L_2</math>-dissimilarity of 0.06. Higher is better. The best defense against each attack is typeset in boldface.]]<br />
<br />
==GrayBox - Image Transformation at Training and Test Time ==<br />
In this experiment, the adversary has access to the network and the related parameters (but does not have access to the input transformations applied at test time). From the network trained in-(BlackBox: Image Transformation at Training and Test Time), novel adversarial images were generated by the four attack methods. The results show that Bit-Depth Reduction and JPEG Compression are weak defenses in such a gray box setting. In contrast, image cropping, rescaling, variation minimization, and image quilting are more robust against adversarial images in this setting.<br />
The results for this experiment are shown in below figure. Networks using these defenses classify up to 50 % of images correctly.<br />
<br />
[[File:sFig6.png|center| 600px |]]<br />
<br />
==Comparison With Ensemble Adversarial Training==<br />
The results of the experiment are compared with the state of the art ensemble adversarial training approach proposed by Tramer et al. [2]. Ensemble Training fits the parameters of a Convolutional Neural Network on adversarial examples that were generated to attack an ensemble of pre-trained models. The model release by Tramer et al [2]: an Inception-Resnet-v2, trained on adversarial examples generated by FGSM against Inception-Resnet-v2 and Inception-v3 models. The authors compared their ResNet-50 models with image cropping, total variance minimization and image quilting defenses. Two assumption differences need to be noticed. Their defenses assume the input transformation is unknown to the adversary and no prior knowledge of the attacks is being used. The results of ensemble training and the pre-processing techniques mentioned in this paper are shown in Table 2. The results show that ensemble adversarial training works better on FGSM attacks (which it uses at training time), but is outperformed by each of the transformation-based defenses all other attacks.<br />
<br />
<br />
<br />
[[File:sTab2.png|600px|thumb|center|Table 2. Top-1 classification accuracy on images perturbed using attacks against ResNet-50 models trained on input-transformed images and an Inception-v4 model trained using ensemble adversarial. Adversarial images are generated by running attacks against the models, aiming for an average normalized <math>L_2</math>-dissimilarity of 0.06. The best defense against each attack is typeset in boldface.]]<br />
<br />
=Discussion/Conclusions=<br />
The paper proposed reasonable approaches to countering adversarial images. The authors evaluated Total Variance Minimization and Image Quilting and compared it with already proposed ideas like Image Cropping - Rescaling, Bit Depth Reduction, JPEG Compression, and Decompression on the challenging ImageNet dataset.<br />
Previous work by Wang et al. [10] shows that a strong input defense should be nondifferentiable and randomized. Two of the defenses - namely Total Variation Minimization and Image Quilting, both possess this property. However, it may still be possible to train a network to perhaps act as an approximation to the non-differentiable transformation. <br />
<br />
Image quilting involves a discrete variable that conducts the selection of a patch from the database, which is a non-differentiable operation.<br />
Additionally, total variation minimization randomly conducts pixels selection from the pixels it uses to measure reconstruction<br />
error during creation of the de-noised image. Image quilting conducts a random selection of a particular K<br />
nearest neighbor uniformly but in a random manner. This inherent randomness makes it difficult to attack the model. <br />
<br />
Future work suggests applying the same techniques to other domains such as speech recognition and image segmentation. For example, in speech recognition, total variance minimization can be used to remove perturbations from waveforms and "spectrogram quilting" techniques that reconstruct a spectrogram could be developed. The proposed input-transformation defenses can also be combined with ensemble adversarial training by Tramèr et al.[2] to study new attack methods.<br />
<br />
=Critiques=<br />
1. The terminology of Black Box, White Box, and Grey Box attack is not exactly given and clear.<br />
<br />
2. White Box attacks could have been considered where the adversary has a full access to the model as well as the pre-processing techniques.<br />
<br />
3. Though the authors did a considerable work in showing the effect of four attacks on ImageNet database, much stronger attacks (Madry et al) [7], could have been evaluated.<br />
<br />
4. Authors claim that the success rate is generally measured as a function of the magnitude of perturbations, performed by the attack using the L2- dissimilarity, but the claim is not supported by any references. None of the previous work has used these metrics.<br />
<br />
5. ([https://openreview.net/forum?id=SyJ7ClWCb])In the new draft of the paper, the authors add the sentence "our defenses assume that part of the defense strategy (viz., the input transformation) is unknown to the adversary".<br />
<br />
This is a completely unreasonable assumption. Any algorithm which hopes to be secure must allow the adversary to, at the very least, understand what the defense is that's being used. Consider a world where the defense here is implemented in practice: any attacker in the world could just go look up the paper, read the description of the algorithm, and know how it works.<br />
<br />
=References=<br />
<br />
1. Chuan Guo , Mayank Rana & Moustapha Ciss´e & Laurens van der Maaten , Countering Adversarial Images Using Input Transformations<br />
<br />
2. Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, Patrick McDaniel, Ensemble Adversarial Training: Attacks and defenses.<br />
<br />
3. Abigail Graese, Andras Rozsa, and Terrance E. Boult. Assessing threat of adversarial examples of deep neural networks. CoRR, abs/1610.04256, 2016. <br />
<br />
4. Qinglong Wang, Wenbo Guo, Kaixuan Zhang, Alexander G. Ororbia II, Xinyu Xing, C. Lee Giles, and Xue Liu. Adversary resistant deep neural networks with an application to malware detection. CoRR, abs/1610.01239, 2016a.<br />
<br />
5. Weilin Xu, David Evans, and Yanjun Qi. Feature squeezing: Detecting adversarial examples in deep neural networks. CoRR, abs/1704.01155, 2017. <br />
<br />
6. Gintare Karolina Dziugaite, Zoubin Ghahramani, and Daniel Roy. A study of the effect of JPG compression on adversarial images. CoRR, abs/1608.00853, 2016.<br />
<br />
7. Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu .Towards Deep Learning Models Resistant to Adversarial Attacks, arXiv:1706.06083v3<br />
<br />
8. Alexei Efros and William Freeman. Image quilting for texture synthesis and transfer. In Proc. SIGGRAPH, pp. 341–346, 2001.<br />
<br />
9. Leonid Rudin, Stanley Osher, and Emad Fatemi. Nonlinear total variation based noise removal algorithms. Physica D, 60:259–268, 1992.<br />
<br />
10. Qinglong Wang, Wenbo Guo, Kaixuan Zhang, Alexander G. Ororbia II, Xinyu Xing, C. Lee Giles, and Xue Liu. Learning adversary-resistant deep neural networks. CoRR, abs/1612.01401, 2016b.<br />
<br />
11. Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song. Delving into transferable adversarial examples and black-box attacks. CoRR, abs/1611.02770, 2016.<br />
<br />
12. Moustapha Cisse, Yossi Adi, Natalia Neverova, and Joseph Keshet. Houdini: Fooling deep structured prediction models. CoRR, abs/1707.05373, 2017 <br />
<br />
13. Marco Melis, Ambra Demontis, Battista Biggio, Gavin Brown, Giorgio Fumera, and Fabio Roli. Is deep learning safe for robot vision? adversarial examples against the icub humanoid. CoRR,abs/1708.06939, 2017.<br />
<br />
14. Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio. Adversarial examples in the physical world. CoRR, abs/1607.02533, 2016b.<br />
<br />
15. Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard. Deepfool: A simple and accurate method to fool deep neural networks. In Proc. CVPR, pp. 2574–2582, 2016.<br />
<br />
16. Nicholas Carlini and David A. Wagner. Towards evaluating the robustness of neural networks. In IEEE Symposium on Security and Privacy, pp. 39–57, 2017.<br />
<br />
17. Ian Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. In Proc. ICLR, 2015.<br />
<br />
18. Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami. Practical black-box attacks against machine learning. In ACM Asia Conference on Computer and Communications Security, 2017.</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Countering_Adversarial_Images_Using_Input_Transformations&diff=42330Countering Adversarial Images Using Input Transformations2018-12-08T01:15:38Z<p>Aghabuss: /* Defenses */</p>
<hr />
<div>The code for this paper is available here[https://github.com/facebookresearch/adversarial_image_defenses]<br />
<br />
==Motivation ==<br />
As the use of machine intelligence has increased, robustness has become a critical feature to guarantee the reliability of deployed machine-learning systems. However, recent research has shown that existing models are not robust to small, adversarially designed perturbations to the input. Adversarial examples are inputs to Machine Learning models so that an attacker has intentionally designed to cause the model to make a mistake. Adversarially perturbed examples have been deployed to attack image classification services (Liu et al., 2016)[11], speech recognition systems (Cisse et al., 2017a)[12], and robot vision (Melis et al., 2017)[13]. The existence of these adversarial examples has motivated proposals for approaches that increase the robustness of learning systems to such examples. In the example below (Goodfellow et. al) [17], a small perturbation is applied to the original image of a panda, changing the prediction to a gibbon.<br />
<br />
[[File:Panda.png|center]]<br />
<br />
==Introduction==<br />
The paper studies strategies that defend against adversarial example attacks on image classification systems by transforming the images before feeding them to a Convolutional Network Classifier. <br />
Generally, defenses against adversarial examples fall into two main categories:<br />
<br />
# Model-Specific – They enforce model properties such as smoothness and invariance via the learning algorithm. <br />
# Model-Agnostic – They try to remove adversarial perturbations from the input. <br />
<br />
Model-specific defense strategies make strong assumptions about expected adversarial attacks. As a result, they violate the Kerckhoffs principle, which states that adversaries can circumvent model-specific defenses by simply changing how an attack is executed. This paper focuses on increasing the effectiveness of model-agnostic defense strategies. Specifically, they investigated the following image transformations as a means for protecting against adversarial images:<br />
<br />
# Image Cropping and Re-scaling (Graese et al, 2016). <br />
# Bit Depth Reduction (Xu et al, 2017) <br />
# JPEG Compression (Dziugaite et al, 2016) <br />
# Total Variance Minimization (Rudin et al, 1992) <br />
# Image Quilting (Efros & Freeman, 2001). <br />
<br />
These image transformations have been studied against Adversarial attacks such as the fast gradient sign method (Goodfelow et. al., 2015), its iterative extension (Kurakin et al., 2016a), Deepfool (Moosavi-Dezfooli et al., 2016), and the Carlini & Wagner (2017) <math>L_2</math>attack. <br />
<br />
The authors in this paper try to focus on increasing the effectiveness of model-agnostic defense strategies through approaches that:<br />
# remove the adversarial perturbations from input images,<br />
# maintain sufficient information in input images to correctly classify them,<br />
# and are still effective in situations where the adversary has information about the defense strategy being used.<br />
<br />
From their experiments, the strongest defenses are based on Total Variance Minimization and Image Quilting. These defenses are non-differentiable and inherently random which makes it difficult for an adversary to get around them.<br />
<br />
==Previous Work==<br />
Recently, a lot of research has focused on countering adversarial threats. Wang et al [4], proposed a new adversary resistant technique that obstructs attackers from constructing impactful adversarial images. This is done by randomly nullifying features within images. Tramer et al [2], showed the state-of-the-art Ensemble Adversarial Training Method, which augments the training process but not only included adversarial images constructed from their model but also including adversarial images generated from an ensemble of other models. Their method implemented on an Inception V2 classifier finished 1st among 70 submissions of NIPS 2017 competition on Defenses against Adversarial Attacks. Graese, et al. [3], showed how input transformation such as shifting, blurring and noise can render the majority of the adversarial examples as non-adversarial. Xu et al.[5] demonstrated, how feature squeezing methods, such as reducing the color bit depth of each pixel and spatial smoothing, defends against attacks. Dziugaite et al [6], studied the effect of JPG compression on adversarial images. Chen et al. [7] introduce an advanced denoising algorithm with GAN based noise modeling in order to improve the blind denoising performance in low-level vision processing. The GAN is trained to estimate the noise distribution over the input noisy images and to generate noise samples. Although meant for image processing, this method can be generalized to target adversarial examples where the unknown noise generating algorithm can be leveraged.<br />
<br />
==Terminology==<br />
<br />
'''Gray Box Attack''' : Model Architecture and parameters are public.<br />
<br />
'''Black Box Attack''': Consider a weak adversary with access to the DNN output only. The adversary has no knowledge<br />
of the architectural choices made to design the DNN, which include the number, type, and size of layers, nor of<br />
the training data used to learn the DNN’s parameters. Such attacks are referred to as black box, where adversaries need<br />
not know internal details of a system to compromise it [18].<br />
<br />
An interesting and important observation of adversarial examples is that they generally are not model or architecture specific. Adversarial examples generated for one neural network architecture will transfer very well to another architecture. In other words, if you wanted to trick a model you could create your own model and adversarial examples based off of it. Then these same adversarial examples will most probably trick the other model as well. This has huge implications as it means that it is possible to create adversarial examples for a completely black box model where we have no prior knowledge of the internal mechanics. [https://ml.berkeley.edu/blog/2018/01/10/adversarial-examples/ reference]<br />
<br />
'''Non Targeted Adversarial Attack''': The goal of the attack is to modify a source image in a way such that the image will be classified incorrectly by the network.<br />
<br />
This is an example on non-targeted adversarial attacks to be more clear [https://ml.berkeley.edu/blog/2018/01/10/adversarial-examples/ reference]:<br />
[[File:non-targeted O.JPG| 600px|center]]<br />
<br />
'''Targeted Adversarial Attack''': The goal of the attack is to modify a source image in way such that image will be classified as a ''target'' class by the network.<br />
<br />
This is an example on targeted adversarial attacks to be more clear [https://ml.berkeley.edu/blog/2018/01/10/adversarial-examples/ reference]:<br />
[[File:Targeted O.JPG| 600px|center]]<br />
<br />
'''Defense''': A defense is a strategy that aims to make the prediction on an adversarial example <math> h(x') </math> equal to the prediction on the corresponding clean example <math> h(x) </math>.<br />
<br />
== Problem Definition ==<br />
The paper discusses non-targeted adversarial attacks for image recognition systems. Given image space <math>\mathcal{X} = [0,1]^{H \times W \times C}</math>, a source image <math>x \in \mathcal{X}</math>, and a classifier <math>h(.)</math>, a non-targeted adversarial example of <math>x</math> is a perturbed image <math>x'</math>, such that <math>h(x) \neq h(x')</math> and <math>d(x, x') \leq \rho</math> for some dissimilarity function <math>d(·, ·)</math> and <math>\rho \geq 0</math>. In the best case scenario, <math>d(·, ·)</math> measures the perceptual difference between the original image <math>x</math> and the perturbed image <math>x'</math>, but usually, Euclidean distance (<math>||x - x'||_2</math>) or the Chebyshov distance (<math>||x - x'||_{\infty}</math>) are used.<br />
<br />
From a set of N clean images <math>[{x_{1}, …, x_{N}}]</math>, an adversarial attack aims to generate <math>[{x'_{1}, …, x'_{N}}]</math> images, such that (<math>x'_{n}</math>) is an adversary of (<math>x_{n}</math>).<br />
<br />
The success rate of an attack is given as: <br />
<br />
<center><math><br />
\frac{1}{N}\sum_{n=1}^{N}I[h(x_n) &ne; h({x_n}^\prime)],<br />
</math></center><br />
<br />
which is the proportions of predictions that were altered by an attack.<br />
<br />
The success rate is generally measured as a function of the magnitude of perturbations performed by the attack. In this paper, L2 perturbations are used and are quantified using the normalized L2-dissimilarity metric:<br />
<math> \frac{1}{N} \sum_{n=1}^N{\frac{\vert \vert x_n - x'_n \vert \vert_2}{\vert \vert x_n \vert \vert_2}} </math><br />
<br />
A strong adversarial attack has a high rate, while its normalized L2-dissimilarity given by the above equation is less.<br />
<br />
In most practical settings, an adversary does not have direct access to the model <math>h(·)</math> and has to do a black-box attack. <br />
<br />
However, prior work has shown successful attacks by transferring adversarial examples generated for a separately-trained model to an unknown target model (Liu et al., 2016), thus allowing efficient black-box attack. <br />
<br />
As a result, the authors investigate both the black-box and a more difficult gray-box attack setting: the adversary has access to the model architecture and the model parameters, but<br />
is unaware of the defence strategy that is being used.<br />
<br />
A defence is an approach that aims make the prediction on an adversarial example <math>h(x')</math> equal to the prediction on the corresponding clean example <math>h(x)</math>. In this study, the authors focus on image transformation defenses <math>g(x)</math> that perform prediction via <math>h(g(x'))</math>. Ideally, <math>g(·)</math> is a complex, non-differentiable, and potentially stochastic function: this makes it difficult for an adversary to attack the prediction model <math>h(g(x))</math> even when the adversary knows both <math>h(·)</math> and <math>g(·)</math>.<br />
<br />
==Adversarial Attacks==<br />
<br />
Although the exact effect that adversarial examples have on the network is unknown, Ian Goodfellow et. al's Deep Learning book states that adversarial examples exploit the linearity of neural networks to perturb the cost function to force incorrect classifications. Images are often high resolution, and thus have thousands of pixels (millions for HD images). An epsilon ball perturbation when dimensionality is in the magnitude of thousands/millions greatly effects the cost function (especially if it increases loss at every pixel). Hence, although the following methods such as FGSM and Iterative FGSM are very straightforward, they greatly influence the network under a white box attack. <br />
<br />
For the experimental purposes, below 4 attacks have been studied in the paper:<br />
<br />
1. '''Fast Gradient Sign Method (FGSM; Goodfellow et al. (2015)) [17]''': Given a source input <math>x</math>, and true label <math>y</math>, and let <math>l(.,.)</math> be the differentiable loss function used to train the classifier <math>h(.)</math>. Then the corresponding adversarial example is given by:<br />
<br />
<math>x' = x + \epsilon \cdot sign(\nabla_x l(x, y))</math><br />
<br />
for some <math>\epsilon \gt 0</math> which controls the perturbation magnitude.<br />
<br />
2. '''Iterative FGSM ((I-FGSM; Kurakin et al. (2016b)) [14]''': iteratively applies the FGSM update, where M is the number of iterations. It is given as:<br />
<br />
<math>x^{(m)} = x^{(m-1)} + \epsilon \cdot sign(\nabla_{x^{m-1}} l(x^{m-1}, y))</math><br />
<br />
where <math>m = 1,...,M; x^{(0)} = x;</math> and <math>x' = x^{(M)}</math>. M is set such that <math>h(x) \neq h(x')</math>.<br />
<br />
Both FGSM and I-FGSM work by minimizing the Chebyshev distance between the inputs and the generated adversarial examples.<br />
<br />
3. '''DeepFool ((Moosavi-Dezfooliet al., 2016) [15]''': projects x onto a linearization of the decision boundary defined by binary classifier h(.) for M iterations. This can be particularly effictive when a network uses ReLU activation functions. It is given as:<br />
<br />
[[File:DeepFool.PNG|400px |]]<br />
<br />
4. '''Carlini-Wagner's L2 attack (CW-L2; Carlini & Wagner (2017)) [16]''': propose an optimization-based attack that combines a differentiable surrogate for the model’s classification accuracy with an L2-penalty term which encourages the adversary image to be close to the original image. Let <math>Z(x)</math> be the operation that computes the logit vector (i.e., the output before the softmax layer) for an input <math>x</math>, and <math>Z(x)_k</math> be the logit value corresponding to class <math>k</math>. The untargeted variant<br />
of CW-L2 finds a solution to the unconstrained optimization problem. It is given as:<br />
<br />
[[File:Carlini.PNG|500px |]]<br />
<br />
As mentioned earlier, the first two attacks minimize the Chebyshev distance whereas the last two attacks minimize the Euclidean distance between the inputs and the adversarial examples.<br />
<br />
All the methods described above maintain <math>x' \in \mathcal{X}</math> by performing value clipping. <br />
<br />
Below figure shows adversarial images and corresponding perturbations at five levels of normalized L2-dissimilarity for all four attacks, mentioned above.<br />
<br />
[[File:Strength.PNG|thumb|center| 600px |Figure 1: Adversarial images and corresponding perturbations at five levels of normalized L2- dissimilarity for all four attacks.]]<br />
<br />
==Defenses==<br />
Defense is a strategy that aims to make the prediction on an adversarial example equal to the prediction on the corresponding clean example, and the particular structure of adversarial perturbations <math> x-x' </math> have been shown in Figure 1.<br />
Five image transformations that alter the structure of these perturbations have been studied:<br />
# Image Cropping and Re-scaling, <br />
# Bit Depth Reduction, <br />
# JPEG Compression, <br />
# Total Variance Minimization, <br />
# Image Quilting.<br />
<br />
'''Image cropping and Rescaling''' has the effect of altering the spatial positioning of the adversarial perturbation which is important in making attacks successful. In this study, images are cropped and re-scaled during training time as part of data-augmentation. At test time, the predictions of randomly cropped are averaged.<br />
<br />
'''Bit Depth Reduction (Xu et. al) [5]''' performs a simple type of quantization that can remove small (adversarial) variations in pixel values from an image. Images are reduced to 3 bits in the experiment.<br />
<br />
'''JPEG Compression and Decompression (Dziugaite etal., 2016)''' removes small perturbations by performing simple quantization. The authors use a quality level of 75/100 in their experiments<br />
<br />
'''Total Variance Minimization (Rudin et. al) [9]''' :<br />
This combines pixel dropout with total variance minimization. This approach randomly selects a small set of pixels, and reconstructs the “simplest” image that is consistent with the selected pixels. The reconstructed image does not contain the adversarial perturbations because these perturbations tend to be small and localized.Specifically, we first select a random set of pixels by sampling a Bernoulli random variable <math>X(i; j; k)</math> for each pixel location <math>(i; j; k)</math>;we maintain a pixel when <math>(i; j; k)</math>= 1. Next, we use total variation, minimization to constructs an image z that is similar to the (perturbed) input image x for the selected<br />
set of pixels, whilst also being “simple” in terms of total variation by solving:<br />
<br />
[[File:TV!.png|300px|]] , <br />
<br />
where <math>TV_{p}(z)</math> represents <math>L_{p}</math> total variation of '''z''' :<br />
<br />
[[File:TV2.png|500px|]]<br />
<br />
The total variation (TV) measures the amount of fine-scale variation in the image z, as a result of which TV minimization encourages removal of small (adversarial) perturbations in the image. The objective function is convex in <math>z</math>, which makes solving for z straightforward. In the paper, p = 2 and a special-purpose solver based on the split Bregman method (Goldstein & Osher, 2009) to perform total variance minimization efficiently is employed.<br />
The effectiveness of TV minimization is illustrated by the images in the middle column of the figure below: in particular, note that the adversarial perturbations that were present in the background for the non- transformed image (see bottom-left image) have nearly completely disappeared in the TV-minimized adversarial image (bottom-center image). As expected, TV minimization also changes image structure in non-homogeneous regions of the image, but as these perturbations were not adversarially designed we expect the negative effect of these changes to be limited.<br />
<br />
[[File:tvx.png]]<br />
<br />
The figure above represents an illustration of total variance minimization and image quilting applied to an original and an adversarial image (produced using I-FGSM with ε = 0.03, corresponding to a normalized L2 - dissimilarity of 0.075). From left to right, the columns correspond to (1) no transformation, (2) total variance minimization, and (3) image quilting. From top to bottom, rows correspond to: (1) the original image, (2) the corresponding adversarial image produced by I-FGSM, and (3) the absolute difference between the two images above. Difference images were multiplied by a constant scaling factor to increase visibility.<br />
<br />
<br />
'''Image Quilting (Efros & Freeman, 2001) [8]'''<br />
Image Quilting is a non-parametric technique that synthesizes images by piecing together small patches that are taken from a database of image patches. The algorithm places appropriate patches in the database for a predefined set of grid points and computes minimum graph cuts in all overlapping boundary regions to remove edge artifacts. Image Quilting can be used to remove adversarial perturbations by constructing a patch database that only contains patches from "clean" images ( without adversarial perturbations); the patches used to create the synthesized image are selected by finding the K nearest neighbors ( in pixel space) of the corresponding patch from the adversarial image in the patch database, and picking one of these neighbors uniformly at random. The motivation for this defense is that resulting image only contains pixels that were not modified by the adversary - the database of real patches is unlikely to contain the structures that appear in adversarial images.<br />
<br />
If we take a look at the effect of image quilting in the above figure, although interpretation of these images is more complicated due to the quantization errors that image quilting introduces, we can still observe that the absolute differences between quilted original and the quilted adversarial image appear to be smaller in non-homogeneous regions of the image. Based on this observation the authors suggest that TV minimization and image quilting lead to inherently different defenses.<br />
<br />
=Experiments=<br />
<br />
Five experiments were performed to test the efficacy of defenses. The first four experiments consider gray and black box attacks. The gray-box attack applies defenses on input adversarial images for the convolutional networks. The adversary is able to read model architecture and parameters but not the defense strategy. The black-box attack replaces convolutional network by a trained network with image-transformations. The final experiment compares the authors' defenses with prior work. <br />
<br />
'''Set up:'''<br />
Experiments are performed on the ImageNet image classification dataset. The dataset comprises 1.2 million training images and 50,000 test images that correspond to one of 1000 classes. The adversarial images are produced by attacking a ResNet-50 model, with different kinds of attacks mentioned in Section5. The strength of an adversary is measured in terms of its normalized L2-dissimilarity. To produce the adversarial images, L2 dissimilarity for each of the attack was set as below:<br />
<br />
- FGSM. Increasing the step size <math>\epsilon</math>, increases the normalized L2-dissimilarity.<br />
<br />
- I-FGSM. We fix M=10, and increase <math>\epsilon</math> to increase the normalized L2-dissimilarity.<br />
<br />
- DeepFool. We fix M=5, and increase <math>\epsilon</math> to increase the normalized L2-dissimilarity.<br />
<br />
- CW-L2. We fix <math>k</math>=0 and <math>\lambda_{f}</math> =10, and multiply the resulting perturbation <br />
<br />
The hyperparameters of the defenses have been fixed in all the experiments. Specifically the pixel dropout probability was set to <math>p</math>=0.5 and regularization parameter of total variation minimizer <math>\lambda_{TV}</math>=0.03.<br />
<br />
Below figure shows the difference between the set up in different experiments below. The network is either trained on a) regular images or b) transformed images. The different settings are marked by 8.1, 8.2 and 8.3 <br />
[[File:models3.png |center]] <br />
<br />
==GrayBox - Image Transformation at Test Time== <br />
This experiment applies a transformation on adversarial images at test time before feeding them to a ResNet -50 which was trained to classify clean images. Below figure shows the results for five different transformations applied and their corresponding Top-1 accuracy. Few of the interesting observations from the plot are: All of the image transformations partly eliminate the effects of the attack, Crop ensemble gives the best accuracy around 40-60 percent, with an ensemble size of 30. The accuracy of Image Quilting Defense hardly deteriorates as the strength of the adversary increases. However, it does impact accuracy on non-adversarial examples.<br />
<br />
[[File:sFig4.png|center|600px |]]<br />
<br />
==BlackBox - Image Transformation at Training and Test Time==<br />
ResNet-50 model was trained on transformed ImageNet Training images. Before feeding the images to the network for training, standard data augmentation (from He et al) along with bit depth reduction, JPEG Compression, TV Minimization, or Image Quilting were applied on the images. The classification accuracy on the same adversarial images as in the previous case is shown Figure below. (Adversary cannot get this trained model to generate new images - Hence this is assumed as a Black Box setting!). Below figure concludes that training Convolutional Neural Networks on images that are transformed in the same way at test time, dramatically improves the effectiveness of all transformation defenses. Nearly 80 -90 % of the attacks are defended successfully, even when the L2- dissimilarity is high.<br />
<br />
<br />
[[File:sFig5.png|center|600px |]]<br />
<br />
<br />
==Blackbox - Ensembling==<br />
Four networks ResNet-50, ResNet-10, DenseNet-169, and Inception-v4 along with an ensemble of defenses were studied, as shown in Table 1. The adversarial images are produced by attacking a ResNet-50 model. The results in the table conclude that Inception-v4 performs best. This could be due to that network having a higher accuracy even in non-adversarial settings. The best ensemble of defenses achieves an accuracy of about 71% against all the other attacks. The attacks deteriorate the accuracy of the best defenses (a combination of cropping, TVM, image quilting, and model transfer) by at most 6%. Gains of 1-2% in classification accuracy could be found from ensembling different defenses, while gains of 2-3% were found from transferring attacks to different network architectures.<br />
<br />
<br />
[[File:sTab1.png|600px|thumb|center|Table 1. Top-1 classification accuracy of ensemble and model transfer defenses (columns) against four black-box attacks (rows). The four networks we use to classify images are ResNet-50 (RN50), ResNet-101 (RN101), DenseNet-169 (DN169), and Inception-v4 (Iv4). Adversarial images are generated by running attacks against the ResNet-50 model, aiming for an average normalized <math>L_2</math>-dissimilarity of 0.06. Higher is better. The best defense against each attack is typeset in boldface.]]<br />
<br />
==GrayBox - Image Transformation at Training and Test Time ==<br />
In this experiment, the adversary has access to the network and the related parameters (but does not have access to the input transformations applied at test time). From the network trained in-(BlackBox: Image Transformation at Training and Test Time), novel adversarial images were generated by the four attack methods. The results show that Bit-Depth Reduction and JPEG Compression are weak defenses in such a gray box setting. In contrast, image cropping, rescaling, variation minimization, and image quilting are more robust against adversarial images in this setting.<br />
The results for this experiment are shown in below figure. Networks using these defenses classify up to 50 % of images correctly.<br />
<br />
[[File:sFig6.png|center| 600px |]]<br />
<br />
==Comparison With Ensemble Adversarial Training==<br />
The results of the experiment are compared with the state of the art ensemble adversarial training approach proposed by Tramer et al. [2]. Ensemble Training fits the parameters of a Convolutional Neural Network on adversarial examples that were generated to attack an ensemble of pre-trained models. The model release by Tramer et al [2]: an Inception-Resnet-v2, trained on adversarial examples generated by FGSM against Inception-Resnet-v2 and Inception-v3 models. The authors compared their ResNet-50 models with image cropping, total variance minimization and image quilting defenses. Two assumption differences need to be noticed. Their defenses assume the input transformation is unknown to the adversary and no prior knowledge of the attacks is being used. The results of ensemble training and the pre-processing techniques mentioned in this paper are shown in Table 2. The results show that ensemble adversarial training works better on FGSM attacks (which it uses at training time), but is outperformed by each of the transformation-based defenses all other attacks.<br />
<br />
<br />
<br />
[[File:sTab2.png|600px|thumb|center|Table 2. Top-1 classification accuracy on images perturbed using attacks against ResNet-50 models trained on input-transformed images and an Inception-v4 model trained using ensemble adversarial. Adversarial images are generated by running attacks against the models, aiming for an average normalized <math>L_2</math>-dissimilarity of 0.06. The best defense against each attack is typeset in boldface.]]<br />
<br />
=Discussion/Conclusions=<br />
The paper proposed reasonable approaches to countering adversarial images. The authors evaluated Total Variance Minimization and Image Quilting and compared it with already proposed ideas like Image Cropping - Rescaling, Bit Depth Reduction, JPEG Compression, and Decompression on the challenging ImageNet dataset.<br />
Previous work by Wang et al. [10] shows that a strong input defense should be nondifferentiable and randomized. Two of the defenses - namely Total Variation Minimization and Image Quilting, both possess this property. However, it may still be possible to train a network to perhaps act as an approximation to the non-differentiable transformation. <br />
<br />
Image quilting involves a discrete variable that conducts the selection of a patch from the database, which is a non-differentiable operation.<br />
Additionally, total variation minimization randomly conducts pixels selection from the pixels it uses to measure reconstruction<br />
error during creation of the de-noised image. Image quilting conducts a random selection of a particular K<br />
nearest neighbor uniformly but in a random manner. This inherent randomness makes it difficult to attack the model. <br />
<br />
Future work suggests applying the same techniques to other domains such as speech recognition and image segmentation. For example, in speech recognition, total variance minimization can be used to remove perturbations from waveforms and "spectrogram quilting" techniques that reconstruct a spectrogram could be developed. The proposed input-transformation defenses can also be combined with ensemble adversarial training by Tramèr et al.[2] to study new attack methods.<br />
<br />
=Critiques=<br />
1. The terminology of Black Box, White Box, and Grey Box attack is not exactly given and clear.<br />
<br />
2. White Box attacks could have been considered where the adversary has a full access to the model as well as the pre-processing techniques.<br />
<br />
3. Though the authors did a considerable work in showing the effect of four attacks on ImageNet database, much stronger attacks (Madry et al) [7], could have been evaluated.<br />
<br />
4. Authors claim that the success rate is generally measured as a function of the magnitude of perturbations, performed by the attack using the L2- dissimilarity, but the claim is not supported by any references. None of the previous work has used these metrics.<br />
<br />
5. ([https://openreview.net/forum?id=SyJ7ClWCb])In the new draft of the paper, the authors add the sentence "our defenses assume that part of the defense strategy (viz., the input transformation) is unknown to the adversary".<br />
<br />
This is a completely unreasonable assumption. Any algorithm which hopes to be secure must allow the adversary to, at the very least, understand what the defense is that's being used. Consider a world where the defense here is implemented in practice: any attacker in the world could just go look up the paper, read the description of the algorithm, and know how it works.<br />
<br />
=References=<br />
<br />
1. Chuan Guo , Mayank Rana & Moustapha Ciss´e & Laurens van der Maaten , Countering Adversarial Images Using Input Transformations<br />
<br />
2. Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, Patrick McDaniel, Ensemble Adversarial Training: Attacks and defenses.<br />
<br />
3. Abigail Graese, Andras Rozsa, and Terrance E. Boult. Assessing threat of adversarial examples of deep neural networks. CoRR, abs/1610.04256, 2016. <br />
<br />
4. Qinglong Wang, Wenbo Guo, Kaixuan Zhang, Alexander G. Ororbia II, Xinyu Xing, C. Lee Giles, and Xue Liu. Adversary resistant deep neural networks with an application to malware detection. CoRR, abs/1610.01239, 2016a.<br />
<br />
5. Weilin Xu, David Evans, and Yanjun Qi. Feature squeezing: Detecting adversarial examples in deep neural networks. CoRR, abs/1704.01155, 2017. <br />
<br />
6. Gintare Karolina Dziugaite, Zoubin Ghahramani, and Daniel Roy. A study of the effect of JPG compression on adversarial images. CoRR, abs/1608.00853, 2016.<br />
<br />
7. Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu .Towards Deep Learning Models Resistant to Adversarial Attacks, arXiv:1706.06083v3<br />
<br />
8. Alexei Efros and William Freeman. Image quilting for texture synthesis and transfer. In Proc. SIGGRAPH, pp. 341–346, 2001.<br />
<br />
9. Leonid Rudin, Stanley Osher, and Emad Fatemi. Nonlinear total variation based noise removal algorithms. Physica D, 60:259–268, 1992.<br />
<br />
10. Qinglong Wang, Wenbo Guo, Kaixuan Zhang, Alexander G. Ororbia II, Xinyu Xing, C. Lee Giles, and Xue Liu. Learning adversary-resistant deep neural networks. CoRR, abs/1612.01401, 2016b.<br />
<br />
11. Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song. Delving into transferable adversarial examples and black-box attacks. CoRR, abs/1611.02770, 2016.<br />
<br />
12. Moustapha Cisse, Yossi Adi, Natalia Neverova, and Joseph Keshet. Houdini: Fooling deep structured prediction models. CoRR, abs/1707.05373, 2017 <br />
<br />
13. Marco Melis, Ambra Demontis, Battista Biggio, Gavin Brown, Giorgio Fumera, and Fabio Roli. Is deep learning safe for robot vision? adversarial examples against the icub humanoid. CoRR,abs/1708.06939, 2017.<br />
<br />
14. Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio. Adversarial examples in the physical world. CoRR, abs/1607.02533, 2016b.<br />
<br />
15. Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard. Deepfool: A simple and accurate method to fool deep neural networks. In Proc. CVPR, pp. 2574–2582, 2016.<br />
<br />
16. Nicholas Carlini and David A. Wagner. Towards evaluating the robustness of neural networks. In IEEE Symposium on Security and Privacy, pp. 39–57, 2017.<br />
<br />
17. Ian Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. In Proc. ICLR, 2015.<br />
<br />
18. Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami. Practical black-box attacks against machine learning. In ACM Asia Conference on Computer and Communications Security, 2017.</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Learning_to_Navigate_in_Cities_Without_a_Map&diff=42329Learning to Navigate in Cities Without a Map2018-12-08T01:12:29Z<p>Aghabuss: /* Future Works */</p>
<hr />
<div>Paper: <br />
[https://arxiv.org/pdf/1804.00168.pdf Learning to Navigate in Cities Without a Map]<br />
A video of the paper is available [https://sites.google.com/view/streetlearn here].<br />
<br />
== Introduction ==<br />
Navigation is an attractive topic in many research disciplines and technology related domains such as neuroscience and robotics. The majority of algorithms are based on the following steps.<br />
<br />
1. Building an explicit map<br />
<br />
2. Planning and acting using that map. <br />
<br />
In this article, based on this fact that human can learn to navigate through cities without using any special tool such as maps or GPS, authors propose new methods to show that a neural network agent can do the same thing by using visual observations. To do so, an interactive environment using Google StreetView Images and a dual pathway agent architecture is designed. As shown in figure 1, some parts of the environment are built using Google StreetView images of New York City (Times Square, Central Park) and London (St. Paul’s Cathedral). The green cone represents the agent’s location and orientation. Although learning to navigate using visual aids is shown to be successful in some domains such as games and simulated environments using deep reinforcement learning (RL), it suffers from data inefficiency and sensitivity to changes in the environment. Thus, it is unclear whether this method could be used for large-scale navigation. That’s why it became the subject of investigation in this paper.<br />
[[File:figure1-soroush.png|600px|thumb|center|Figure 1. Our environment is built of real-world places from StreetView. The figure shows diverse views and corresponding local maps (neither map nor current position have not been used by the agent) in New York City (Times Square, Central Park) and London (St. Paul’s Cathedral). The green cone represents the agent’s location and orientation.]]<br />
<br />
==Contribution==<br />
This paper has made the following contributions:<br />
<br />
1. Designing a dual pathway agent architecture. This agent can navigate through a real city and is trained with end-to-end reinforcement learning to handle real-world navigations.<br />
<br />
2. Using Goal-dependent learning. This means that the policy and value functions must adapt themselves to a sequence of goals that are provided as input.<br />
<br />
3. Leveraging a recurrent neural architecture. Using that, not only could navigation through a city be possible, but also the model is scalable for navigation in new cities. This architecture supports both locale-specific learnings and general transferable navigations. The authors achieved these by separating a recurrent neural pathway. This pathway receives and interprets the current goal as well as encapsulates and memorizes features of a single region.<br />
<br />
4. Using a new environment which is built on top of Google StreetView images. This provides real-world images for agent’s observation. Using this environment, the agent can navigate from an arbitrary starting point to a goal and then to another goal etc. Also, London, Paris, and New York City are chosen for navigation.<br />
<br />
The authors demonstrate that their proposed method can provide a mechanism for transferring knowledge to new cities. As with humans, when the agent visits a new city, the expectation is it to have it learn a new set of landmarks, but not to have to re-learn its visual representations or its behaviours (e.g., zooming forward along streets or turning at intersections). Therefore, using the MultiCity architecture, the paper trains first on a number of cities, then freezes both the policy network and the visual convolutional network and only a new locale-specific pathway on a new city. This approach enables the agent to acquire new knowledge without forgetting what it has already learned, similarly to the progressive neural networks architecture.<br />
<br />
==Related Work==<br />
<br />
1. Localization from real-world imagery. For example, (Weyand et al., 2016), a CNN was able to achieve excellent results on geolocation task. This paper provides novel work by not including supervised training with ground-truth labels, and by including planning as a goal. Some other works also improve by exploiting spatiotemporal continuity or estimating camera pose or depth estimation from pixels. These methods rely on supervised training with ground truth labels, which is not possible in every environment. <br />
<br />
2. Deep RL methods for navigation. For instance, (Mirowski et al., 2016; Jaderberg et al., 2016) used self-supervised auxiliary tasks to produce visual navigation in several created mazes. Some other researches used text descriptions to incorporate goal instructions. Researchers developed realistic, higher-fidelity environment simulations to make the experiment more realistic, but that still came with lack of diversities. This paper makes use of real-world data, in contrast to many related papers in this area. It's diverse and visually realistic but still, it does not contain dynamic elements, and the street topology cannot be regenerated or altered.<br />
<br />
3. Deep RL for path planning and mapping. For example, (Zhang et al., 2017) created an agent that represented a global map via an RL agent with external memory; some other work uses a hierarchical control strategy to propose a structured memory and Memory Augmented Control Maps. Explicit neural mapper and navigation planner with joint training was also used. Among all these works, the target-driven visual navigation with a goal-conditional policy approach was most related to our method.<br />
<br />
4. To make simulations resemble reality, researchers have developed higher-fidelity simulated environments (Dosovitskiy et al., 2017; Kolve et al., 2017; Shah et al., 2018; Wu et al., 2018). However, in spite of the photo-realism, the inherent problems of simulated environments pertain to the limited diversity of the environments and the idealistic cleanliness of the observations.<br />
<br />
==Environment==<br />
Google StreetView consists of both high-resolution 360-degree imagery and graph connectivity. Also, it provides a public API. These features make it a valuable resource. In this work, large areas of New York, Paris, and London that contain between 7,000 and 65,500 nodes<br />
(and between 7,200 and 128,600 edges, respectively), have a mean node spacing of 10m and cover a range of up to<br />
5km chosen (Figure 2), without simplifying the underlying connections. This means that there are many areas 'congested' with nodes, occlusions, available footpaths, etc. The agent only sees RGB images that are visible in StreetView images (Figure 1) and is not aware of the underlying graph.<br />
<br />
[[File:figure2-soroush.png|700px|thumb|center|Figure 2. Map of the 5 environments in New York City; our experiments focus on the NYU area as well as on transfer learning from the other areas to Wall Street (see Section 5.3). In the zoomed in area, each green dot corresponds to a unique panorama, the goal is marked in blue, and landmark locations are marked with red pins.]]<br />
<br />
==Agent Interface and the Courier Task==<br />
In an RL environment, we need to define observations and actions in addition to tasks. The inputs to the agent are the image <math>x_t</math> and the goal <math>g_t</math>. Also, a first-person view of the 3D environment is simulated by cropping <math>x_t</math> to a 60-degree square RGB image that is scaled to 84*84 pixels. Furthermore, the action space consists of 5 movements: “slow” rotate left or right (±22:5), “fast” rotate left or right (±67.5), or move forward (implemented as a ''noop'' in the case where this is not a viable action). The most central edge is chosen if there are multiple edges in the agents viewing cone.<br />
<br />
There are lots of ways to specify the goal to the agent. In this paper, the current goal is chosen to be represented in terms of its proximity to a set L of fixed landmarks <math> L={(Lat_k, Long_k)}</math> which are specified using Latitude and Longitude coordinate system. For distance to the <math> k_{th}</math> landmark <math>{(d_{(t,k)}^g})_k</math> the goal vector contains <math> g_{(t,i)}=\tfrac{exp(-αd_{(t,i)}^g)}{∑_k exp(-αd_{(t,k)}^g)} </math>for <math>i_{th}</math> landmark with <math>α=0.002</math> (Figure 3).<br />
<br />
[[File:figure3-soroush.PNG|400px|thumb|center|Figure 3. We illustrate the goal description by showing a goal and a set of 5 landmarks that are nearby, plus 4 that are more distant. The code <math>g_i</math> is a vector with a softmax-normalised distance to each landmark.]]<br />
<br />
This form of representation has several advantages: <br />
<br />
1. It could easily be extended to new environments.<br />
<br />
2. It is intuitive. Even humans and animals use landmarks to be able to move from one place to another.<br />
<br />
3. It does not rely on arbitrary map coordinates, and provides an absolute (as opposed to relative) goal.<br />
<br />
In this work, 644 landmarks for New York, Paris, and London are manually defined. The courier task is the problem of navigating to a list of random locations within a city. In each episode, which consists of 1000 steps, the agent starts from a random place with random orientation. when an agent gets within 100 meters of goal, the next goal is randomly chosen. An episode ends after 1000 agent steps. Finally, the reward is proportional to the shortest path between agent and goal when the goal is first assigned (providing more reward for longer journeys). Thus the agent needs to learn the mapping between the images observed at the goal location and the goal vector in order to solve the courier task problem. Furthermore, the agent must learn the association between the images observed at its current location and the policy to reach the goal destination.<br />
<br />
==Methods==<br />
<br />
===Goal-dependent Actor-Critic Reinforcement Learning===<br />
In this paper, the learning problem is based on Markov Decision Process, with state space <math>\mathcal{S}</math>, action space <math>\mathcal{A}</math>, environment <math>\mathcal{E}</math>, and a set of possible goals <math>\mathcal{G}</math>. The reward function depends on the current goal and state: <math>\mathcal{R}: \mathcal{S} \times \mathcal{G} \times \mathcal{A} &rarr; \mathbb{R}</math>. Typically, in reinforcement learning the main goal is to find the policy which maximizes the expected return. Expected return is defined as the sum of<br />
discounted rewards starting from state <math>s_0</math> with discount <math>\gamma</math>. Also, the expected return from a state <math>s_t</math> depends on the goals that are sampled. The policy is defined as a distribution over the actions, given the current state <math>s_t</math> and the goal <math>g_t</math>: <br />
<br />
\begin{align}<br />
\pi(\alpha|s,g)=Pr(\alpha_t=\alpha|s_t=s, g_t=g)<br />
\end{align}<br />
<br />
Value function is defined as the expected return obtained by sampling actions from policy <math>\pi</math> from state <math>s_t</math> with goal <math>g_t</math>:<br />
<br />
\begin{align}<br />
V^{\pi}(s,g)=E[R_t]=E[Σ_{k=0}^{\infty}\gamma^kr_{t+k}|s_t=s, g_t=g]<br />
\end{align}<br />
<br />
Also, an architecture with multiple pathways is designed to support two types of learning that is required for this problem. First, an agent needs an internal representation which is general and gives an understanding of a scene. Second, to better understand a scene the agent needs to remember unique features of the scene which then help the agent to organize and remember the scenes.<br />
<br />
===Architectures===<br />
<br />
[[File:figure4-soroush.png|400px|thumb|center|Figure 4. Comparison of architectures. Left: GoalNav is a convolutional encoder plus policy LSTM with goal description input. Middle: CityNav is a single-city navigation architecture with a separate goal LSTM and optional auxiliary heading (θ). Right: MultiCityNav is a multi-city architecture with individual goal LSTM pathways for each city.]]<br />
<br />
The authors use neural networks to parameterize policy and value functions. These neural networks share weights in all layers except the final linear layer. The agent takes image pixels as input. These pixels are passed through a convolutional network. The output of the Convolution network is fed to a Long Short-Term Memory (LSTM) as well as the past reward <math>r_{t-1}</math> and previous action <math>\alpha_{t-1}</math>.<br />
<br />
Three different architectures are described below.<br />
<br />
The '''GoalNav''' architecture (Fig. 4a) which consists of a convolutional architecture and policy LSTM. Goal description <math>g_t</math>, previous action, and reward are the inputs of this LSTM.<br />
<br />
The '''CityNav''' architecture (Fig. 4b) consists of the previous architecture alongside an additional LSTM, called the goal LSTM. Inputs of this LSTM are visual features and the goal description. The CityNav agent also adds an auxiliary heading (θ) prediction task which is defined as an angle between the north direction and the agent’s pose. This auxiliary task can speed up learning and provides relevant information. <br />
<br />
The '''MultiCityNav''' architecture (Fig. 4c) is an extension of CityNav for learning in different cities. This is done using the parallel connection of goal LSTMs for encapsulating locale-specific features, for each city. Moreover, the convolutional architecture and the policy LSTM become general after training on a number of cities. So, new goal LSTMs are required to be trained in new cities.<br />
<br />
In this paper, the authors use IMPALA [1] to train the agents because IMPALA can get similar performance to A3C [2].<br />
<br />
===Prior on agent training: IMPALA and A3C===<br />
<br />
IMPALA (Importance Weighted Actor-Learner Architecture) is an actor-critic implementation of deep reinforcement learning that decouples actions from learning. IMPALA results in a comparable performance to A3C (Google DeepMind's previous algorithm: Asynchronous Actor-Critic Agents) on a single city task, but it has been shown to handle better multi-task learning than A3C. The authors use 256 actors for CityNav and 512 actors for MultiCityNav, with batch sizes of 256 or 512 respectively, and sequences are unrolled to length 50.<br />
<br />
===Curriculum Learning===<br />
In curriculum learning, the model is trained using simple examples in first steps. As soon as the model learns those examples, more complex and difficult examples would be fed to the model. In this paper, this approach is used to teach agent to navigate to further destinations. This courier task suffers from a common problem of RL tasks which is sparse rewards (similar to Montezuma’s Revenge) . To overcome this problem, a natural curriculum scheme is defined, in which sampling each new goal would be within 500m of the agent’s position. This is called phase 1. In phase 2, the maximum range is gradually increased to cover the full graph (3.5km in the smaller New York areas, or 5km for central London or Downtown Manhattan)<br />
<br />
Curriculum learning was first introduced by Bengio et. al in 2009. It serves as a continuation method for non-convex optimization, and improves training time by injecting noisy data. One example outside this paper for curriculum learning is outlined below:<br />
<br />
1. We aim to classify shapes within the following three classes: triangles, ellipses, and rectangles. We can create a curriculum by first starting with a simplified dataset that consists of only special cases of these three classes: equilateral triangles, circles, and squares. By first training on these special cases, and then introducing the full model, we can allow the algorithm to converge more quickly towards a local minima before providing "harder" examples. Feeding only these specialized examples also serves as a method to make the classes fall on more distinct manifold locations; with less overlap, these networks will perform better when noise is later added as well.<br />
<br />
==Results==<br />
In this section, the performance of the proposed architectures on the courier task is shown.<br />
<br />
[[File:figure5-2.png|600px|thumb|center|Figure 5. Average per-episode goal rewards (y-axis) are plotted vs. learning steps (x-axis) for the courier task in the NYU (New York City) environment (top), and in central London (bottom). We compare the GoalNav agent, the CityNav agent, and the CityNav agent without skip connection on the NYU environment, and the CityNav agent in London. We also compare the Oracle performance and a Heuristic agent, described below. The London agents were trained with a 2-phase curriculum– we indicate the end of phase 1 (500m only) and the end of phase 2 (500m to 5000m). Results on the Rive Gauche part of Paris (trained in the same way<br />
as in London) are comparable and the agent achieved mean goal reward 426.]]<br />
<br />
It is first shown that the CityNav agent, trained with curriculum learning, succeeds in learning the courier task in New York, London and Paris. Figure 5 compares the following agents:<br />
<br />
1. Goal Navigation agent.<br />
<br />
2. City Navigation Agent.<br />
<br />
3. A City Navigation agent without the skip connection from the vision layers to the policy LSTM. This is needed to regularise the interface between the goal LSTM and the policy LSTM in multi-city transfer scenario.<br />
<br />
Also, a lower bound (Heuristic) and an upper bound(Oracle) on the performance is considered. As it is said in the paper: "Heuristic is a random walk on the street graph, where the agent turns in a random direction if it cannot move forward; if at an intersection it will turn with a probability <math>P=0.95</math>. Oracle uses the full graph to compute the optimal path using breadth-first search.". As it is clear in Figure 5, CityNav architecture with the previously mentioned architecture attains a higher performance and is more stable than the simpler GoalNav agent.<br />
<br />
The trajectories of the trained agent over two 1000 step episodes and the value function of the agent during navigation to a destination is shown in Figure 6.<br />
<br />
[[File:figure6-soroush.png|400px|thumb|center|Figure 6. Trained CityNav agent’s performance in two environments: Central London (left panes), and NYU (right panes). Top: examples of the agent’s trajectory during one 1000-step episode, showing successful consecutive goal acquisitions. The arrows show the direction of travel of the agent. Bottom: We visualize the value function of the agent during 100 trajectories with random starting points and the same goal (respectively St Paul’s Cathedral and Washington Square). Thicker and warmer color lines correspond to higher value functions.]]<br />
<br />
Figure 7 shows that navigation policy is learned by agent successfully in St Paul’s Cathedral in London and Washington Square in New York.<br />
[[File:figure7-soroush.png|400px|thumb|center|Figure 7. Number of steps required for the CityNav agent to reach<br />
a goal (Washington Square in New York or St Paul’s Cathedral in<br />
London) from 100 start locations vs. the straight-line distance to<br />
the goal in meters. One agent step corresponds to a forward movement<br />
of about 10m or a left/right turn by 22.5 or 67.5 degrees.]]<br />
<br />
The authors mask 25% of the possible goals and train on the remaining ones in order to investigate the generalisation capability of a trained agent. Figure 8 Showa that the agent is still able to traverse through these areas, it just never samples a goal there. <br />
[[File:fff8.png|600px|center]]<br />
<br />
A critical test for this article is to transfer model to new cities by learning a new set of landmarks, but without re-learning visual representation, behaviors, etc. Therefore, the MultiCityNav agent is trained on a number of cities besides freezing both the policy LSTM and the convolutional encoder. Then a new locale-specific goal LSTM is trained. The performance is compared using three different training regimes, illustrated in Fig. 9: Training on only the target city (single training); training on multiple cities, including the target city, together (joint training); and joint training on all but the target city, followed by training on the target city with the rest of the architecture frozen (pre-train and transfer). Figure 10 shows that transferring to other cities is possible. Also, training the model on more cities would increase its effectiveness. According to the paper: "Remarkably, the agent that is pre-trained on 4 regions and then transferred to Wall Street achieves comparable performance to an agent trained jointly on all the regions, and only slightly worse than single-city training on Wall Street alone". Training the model in a single city using skip connection is useful. However, it is not useful in multi-city transferring.<br />
[[File:figure9-soroush.png|400px|thumb|center|Figure 9. Illustration of training regimes: (a) training on a single city (equivalent to CityNav); (b) joint training over multiple cities with a dedicated per-city pathway and shared convolutional net and policy LSTM; (c) joint pre-training on a number of cities followed by training on a target city with convolutional net and policy LSTM frozen (only the target city pathway is optimized).]]<br />
[[File:figure10-soroush.png|400px|thumb|center|Figure 10. Joint multi-city training and transfer learning performance of variants of the MultiCityNav agent evaluated only on the target city (Wall Street). We compare single-city training on the target environment alone vs. joint training on multiple cities (3, 4, or 5-way joint training including Wall Street), vs. pre-training on multiple cities and then transferring to Wall Street while freezing the entire agent except for the new pathway (see Fig. 10). One variant has skip connections between the convolutional encoder and the policy LSTM, the other does not (no-skip).]]<br />
<br />
Giving early rewards before agent reaches the goal or adding random rewards (coins) to encourage exploration is investigated in this article. Figure 11a suggests that coins by themselves are ineffective as our task does not benefit from wide explorations. Also, as it is clear from Figure 11b, reducing the density of the landmarks does not seem to reduce the performance. Based on the results, authors chose to start sampling the goal within a radius of 500m from the agent’s location, and then progressively extend it to the maximum distance an agent could travel within the environment. In addition, to asses the importance of the goal-conditioned agents, a Goal-less CityNav agent is trained by removing inputs gt. The poor performance of this agent is clear in Figure 11b. Furthermore, reducing the density of the landmarks by the ratio of 50%, 25%, and 12:5% does not reduce the performance that much. Finally, some alternative for goal representation is investigated:<br />
<br />
a) Latitude and longitude scalar coordinates normalized to be between 0 and 1. This is based on the region which the agent navigates.<br />
<br />
b) Binned representation. <br />
<br />
The latitude and longitude scalar goal representations perform the best. However, since the all landmarks representation performs well while remaining independent of the coordinate system, we use this representation as the canonical one.<br />
<br />
[[File:figure11-soroush.PNG|300px|thumb|center|Figure 11. Top: Learning curves of the CityNav agent on NYU, comparing reward shaping with different radii of early rewards (ER) vs. ER with random coins vs. curriculum learning with ER 200m and no coins (ER 200m, Curr.). Bottom: Learning curves for CityNav agents with different goal representations: landmark-based, as well as latitude and longitude classification-based and regression-based.]]<br />
<br />
==Conclusion==<br />
In this paper, a deep reinforcement learning approach that enables navigation in cities is presented through the use of Google StreetView for its photographic content and worldwide coverage. Furthermore, the authors discussed a new courier task and a multi-city neural network agent architecture that is transferable to new cities. A successful navigation architecture is presented which relies on integration of general policies with locale-specific knowledge.<br />
<br />
==Future Works==<br />
The paper uses staic Google Street View images. However, this means that there are some more information that we can get from the images beyond the route. Even though it is not the central focus of the paper, it would be extremely useful if we can incorporate such information for effective route-building or planning.<br />
<br />
[[File:picture1.png|400px|thumb|center|Figure 12. LearningcurvesoftheCityNavagent(2LSTM+Skip+HD) on NYU, comparing different ablations, allthey way down toGoalNav(LSTM). 2LSTM architectures havea global pathway LSTM and a policy LSTM with optional Skipconnection between the convnet and the policy LSTM. HD is theheading prediction auxiliary task.]]<br />
<br />
==Critique==<br />
1. It is not clear how this model is applicable to the real world. A real-world navigation problem needs to detect objects, people, and cars. However, it is not clear whether they are modeling them or not. From what I understood, they did not care about the collision, which is against their claim that it is a real-world problem.<br />
<br />
2. This paper is only using static Google Street View images as its primary source of data. But the authors must at least complement this with other dynamic data like traffic and road blockage information for a realistic model of navigation in the world. Also, this is quite understandable not to use maps but is not clear why have they not used GPS to know their position and maybe even made up with a map. This can be something useful in an emergency or even for investigating places that are not known or there is no access to them. The resulting map could be easily compared with the real one and could also be used in training to achieve higher performance. The availability should not be a serious problem because if they are simulating a real city and the google images are available, why should not GPS be? What is the intuition? At least, a complementary description on this could be helpful.<br />
<br />
3. The 'Transfer in Multi-City Experiments' results could be strengthened significantly via cross-validation (only Wall Street, which covers the smallest area of the four regions, is used as the test case). Additionally, the results do not show true 'multi-city' transfer learning, since all regions are within New York City. It is stated in the paper that not having to re-learn visual representations when transferring between cities is one of the outcomes, but the tests do not actually check for this. There are likely significant differences in the features that would be learned in NYC vs. Waterloo, for example, and this type of transfer has not been evaluated.<br />
<br />
4. The proposed navigation model could be limited by its reliance on pre-defined landmarks, which appears to be strategically placed evenly spreading across each city. This could limit the agent's deployability to new cities.<br />
<br />
==Reference==<br />
[1] Espeholt, Lasse, Soyer, Hubert, Munos, Remi, Simonyan, Karen, Mnih, Volodymir, Ward, Tom, Doron, Yotam, Firoiu, Vlad, Harley, Tim, Dunning, Iain, Legg, Shane, and Kavukcuoglu, Koray. Impala: Scalable distributed deep-rl with importance weighted actor-learner architec- tures. arXiv preprint arXiv:1802.01561, 2018.<br />
<br />
[2] Mnih, Volodymyr, Badia, Adria Puigdomenech, Mirza, Mehdi, Graves, Alex, Lillicrap, Timothy, Harley, Tim, Silver, David, and Kavukcuoglu, Koray. Asynchronous methods for deep reinforcement learning. In Interna- tional Conference on Machine Learning, pp. 1928–1937, 2016.</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Learning_to_Navigate_in_Cities_Without_a_Map&diff=42328Learning to Navigate in Cities Without a Map2018-12-08T01:12:17Z<p>Aghabuss: /* Future Works */</p>
<hr />
<div>Paper: <br />
[https://arxiv.org/pdf/1804.00168.pdf Learning to Navigate in Cities Without a Map]<br />
A video of the paper is available [https://sites.google.com/view/streetlearn here].<br />
<br />
== Introduction ==<br />
Navigation is an attractive topic in many research disciplines and technology related domains such as neuroscience and robotics. The majority of algorithms are based on the following steps.<br />
<br />
1. Building an explicit map<br />
<br />
2. Planning and acting using that map. <br />
<br />
In this article, based on this fact that human can learn to navigate through cities without using any special tool such as maps or GPS, authors propose new methods to show that a neural network agent can do the same thing by using visual observations. To do so, an interactive environment using Google StreetView Images and a dual pathway agent architecture is designed. As shown in figure 1, some parts of the environment are built using Google StreetView images of New York City (Times Square, Central Park) and London (St. Paul’s Cathedral). The green cone represents the agent’s location and orientation. Although learning to navigate using visual aids is shown to be successful in some domains such as games and simulated environments using deep reinforcement learning (RL), it suffers from data inefficiency and sensitivity to changes in the environment. Thus, it is unclear whether this method could be used for large-scale navigation. That’s why it became the subject of investigation in this paper.<br />
[[File:figure1-soroush.png|600px|thumb|center|Figure 1. Our environment is built of real-world places from StreetView. The figure shows diverse views and corresponding local maps (neither map nor current position have not been used by the agent) in New York City (Times Square, Central Park) and London (St. Paul’s Cathedral). The green cone represents the agent’s location and orientation.]]<br />
<br />
==Contribution==<br />
This paper has made the following contributions:<br />
<br />
1. Designing a dual pathway agent architecture. This agent can navigate through a real city and is trained with end-to-end reinforcement learning to handle real-world navigations.<br />
<br />
2. Using Goal-dependent learning. This means that the policy and value functions must adapt themselves to a sequence of goals that are provided as input.<br />
<br />
3. Leveraging a recurrent neural architecture. Using that, not only could navigation through a city be possible, but also the model is scalable for navigation in new cities. This architecture supports both locale-specific learnings and general transferable navigations. The authors achieved these by separating a recurrent neural pathway. This pathway receives and interprets the current goal as well as encapsulates and memorizes features of a single region.<br />
<br />
4. Using a new environment which is built on top of Google StreetView images. This provides real-world images for agent’s observation. Using this environment, the agent can navigate from an arbitrary starting point to a goal and then to another goal etc. Also, London, Paris, and New York City are chosen for navigation.<br />
<br />
The authors demonstrate that their proposed method can provide a mechanism for transferring knowledge to new cities. As with humans, when the agent visits a new city, the expectation is it to have it learn a new set of landmarks, but not to have to re-learn its visual representations or its behaviours (e.g., zooming forward along streets or turning at intersections). Therefore, using the MultiCity architecture, the paper trains first on a number of cities, then freezes both the policy network and the visual convolutional network and only a new locale-specific pathway on a new city. This approach enables the agent to acquire new knowledge without forgetting what it has already learned, similarly to the progressive neural networks architecture.<br />
<br />
==Related Work==<br />
<br />
1. Localization from real-world imagery. For example, (Weyand et al., 2016), a CNN was able to achieve excellent results on geolocation task. This paper provides novel work by not including supervised training with ground-truth labels, and by including planning as a goal. Some other works also improve by exploiting spatiotemporal continuity or estimating camera pose or depth estimation from pixels. These methods rely on supervised training with ground truth labels, which is not possible in every environment. <br />
<br />
2. Deep RL methods for navigation. For instance, (Mirowski et al., 2016; Jaderberg et al., 2016) used self-supervised auxiliary tasks to produce visual navigation in several created mazes. Some other researches used text descriptions to incorporate goal instructions. Researchers developed realistic, higher-fidelity environment simulations to make the experiment more realistic, but that still came with lack of diversities. This paper makes use of real-world data, in contrast to many related papers in this area. It's diverse and visually realistic but still, it does not contain dynamic elements, and the street topology cannot be regenerated or altered.<br />
<br />
3. Deep RL for path planning and mapping. For example, (Zhang et al., 2017) created an agent that represented a global map via an RL agent with external memory; some other work uses a hierarchical control strategy to propose a structured memory and Memory Augmented Control Maps. Explicit neural mapper and navigation planner with joint training was also used. Among all these works, the target-driven visual navigation with a goal-conditional policy approach was most related to our method.<br />
<br />
4. To make simulations resemble reality, researchers have developed higher-fidelity simulated environments (Dosovitskiy et al., 2017; Kolve et al., 2017; Shah et al., 2018; Wu et al., 2018). However, in spite of the photo-realism, the inherent problems of simulated environments pertain to the limited diversity of the environments and the idealistic cleanliness of the observations.<br />
<br />
==Environment==<br />
Google StreetView consists of both high-resolution 360-degree imagery and graph connectivity. Also, it provides a public API. These features make it a valuable resource. In this work, large areas of New York, Paris, and London that contain between 7,000 and 65,500 nodes<br />
(and between 7,200 and 128,600 edges, respectively), have a mean node spacing of 10m and cover a range of up to<br />
5km chosen (Figure 2), without simplifying the underlying connections. This means that there are many areas 'congested' with nodes, occlusions, available footpaths, etc. The agent only sees RGB images that are visible in StreetView images (Figure 1) and is not aware of the underlying graph.<br />
<br />
[[File:figure2-soroush.png|700px|thumb|center|Figure 2. Map of the 5 environments in New York City; our experiments focus on the NYU area as well as on transfer learning from the other areas to Wall Street (see Section 5.3). In the zoomed in area, each green dot corresponds to a unique panorama, the goal is marked in blue, and landmark locations are marked with red pins.]]<br />
<br />
==Agent Interface and the Courier Task==<br />
In an RL environment, we need to define observations and actions in addition to tasks. The inputs to the agent are the image <math>x_t</math> and the goal <math>g_t</math>. Also, a first-person view of the 3D environment is simulated by cropping <math>x_t</math> to a 60-degree square RGB image that is scaled to 84*84 pixels. Furthermore, the action space consists of 5 movements: “slow” rotate left or right (±22:5), “fast” rotate left or right (±67.5), or move forward (implemented as a ''noop'' in the case where this is not a viable action). The most central edge is chosen if there are multiple edges in the agents viewing cone.<br />
<br />
There are lots of ways to specify the goal to the agent. In this paper, the current goal is chosen to be represented in terms of its proximity to a set L of fixed landmarks <math> L={(Lat_k, Long_k)}</math> which are specified using Latitude and Longitude coordinate system. For distance to the <math> k_{th}</math> landmark <math>{(d_{(t,k)}^g})_k</math> the goal vector contains <math> g_{(t,i)}=\tfrac{exp(-αd_{(t,i)}^g)}{∑_k exp(-αd_{(t,k)}^g)} </math>for <math>i_{th}</math> landmark with <math>α=0.002</math> (Figure 3).<br />
<br />
[[File:figure3-soroush.PNG|400px|thumb|center|Figure 3. We illustrate the goal description by showing a goal and a set of 5 landmarks that are nearby, plus 4 that are more distant. The code <math>g_i</math> is a vector with a softmax-normalised distance to each landmark.]]<br />
<br />
This form of representation has several advantages: <br />
<br />
1. It could easily be extended to new environments.<br />
<br />
2. It is intuitive. Even humans and animals use landmarks to be able to move from one place to another.<br />
<br />
3. It does not rely on arbitrary map coordinates, and provides an absolute (as opposed to relative) goal.<br />
<br />
In this work, 644 landmarks for New York, Paris, and London are manually defined. The courier task is the problem of navigating to a list of random locations within a city. In each episode, which consists of 1000 steps, the agent starts from a random place with random orientation. when an agent gets within 100 meters of goal, the next goal is randomly chosen. An episode ends after 1000 agent steps. Finally, the reward is proportional to the shortest path between agent and goal when the goal is first assigned (providing more reward for longer journeys). Thus the agent needs to learn the mapping between the images observed at the goal location and the goal vector in order to solve the courier task problem. Furthermore, the agent must learn the association between the images observed at its current location and the policy to reach the goal destination.<br />
<br />
==Methods==<br />
<br />
===Goal-dependent Actor-Critic Reinforcement Learning===<br />
In this paper, the learning problem is based on Markov Decision Process, with state space <math>\mathcal{S}</math>, action space <math>\mathcal{A}</math>, environment <math>\mathcal{E}</math>, and a set of possible goals <math>\mathcal{G}</math>. The reward function depends on the current goal and state: <math>\mathcal{R}: \mathcal{S} \times \mathcal{G} \times \mathcal{A} &rarr; \mathbb{R}</math>. Typically, in reinforcement learning the main goal is to find the policy which maximizes the expected return. Expected return is defined as the sum of<br />
discounted rewards starting from state <math>s_0</math> with discount <math>\gamma</math>. Also, the expected return from a state <math>s_t</math> depends on the goals that are sampled. The policy is defined as a distribution over the actions, given the current state <math>s_t</math> and the goal <math>g_t</math>: <br />
<br />
\begin{align}<br />
\pi(\alpha|s,g)=Pr(\alpha_t=\alpha|s_t=s, g_t=g)<br />
\end{align}<br />
<br />
Value function is defined as the expected return obtained by sampling actions from policy <math>\pi</math> from state <math>s_t</math> with goal <math>g_t</math>:<br />
<br />
\begin{align}<br />
V^{\pi}(s,g)=E[R_t]=E[Σ_{k=0}^{\infty}\gamma^kr_{t+k}|s_t=s, g_t=g]<br />
\end{align}<br />
<br />
Also, an architecture with multiple pathways is designed to support two types of learning that is required for this problem. First, an agent needs an internal representation which is general and gives an understanding of a scene. Second, to better understand a scene the agent needs to remember unique features of the scene which then help the agent to organize and remember the scenes.<br />
<br />
===Architectures===<br />
<br />
[[File:figure4-soroush.png|400px|thumb|center|Figure 4. Comparison of architectures. Left: GoalNav is a convolutional encoder plus policy LSTM with goal description input. Middle: CityNav is a single-city navigation architecture with a separate goal LSTM and optional auxiliary heading (θ). Right: MultiCityNav is a multi-city architecture with individual goal LSTM pathways for each city.]]<br />
<br />
The authors use neural networks to parameterize policy and value functions. These neural networks share weights in all layers except the final linear layer. The agent takes image pixels as input. These pixels are passed through a convolutional network. The output of the Convolution network is fed to a Long Short-Term Memory (LSTM) as well as the past reward <math>r_{t-1}</math> and previous action <math>\alpha_{t-1}</math>.<br />
<br />
Three different architectures are described below.<br />
<br />
The '''GoalNav''' architecture (Fig. 4a) which consists of a convolutional architecture and policy LSTM. Goal description <math>g_t</math>, previous action, and reward are the inputs of this LSTM.<br />
<br />
The '''CityNav''' architecture (Fig. 4b) consists of the previous architecture alongside an additional LSTM, called the goal LSTM. Inputs of this LSTM are visual features and the goal description. The CityNav agent also adds an auxiliary heading (θ) prediction task which is defined as an angle between the north direction and the agent’s pose. This auxiliary task can speed up learning and provides relevant information. <br />
<br />
The '''MultiCityNav''' architecture (Fig. 4c) is an extension of CityNav for learning in different cities. This is done using the parallel connection of goal LSTMs for encapsulating locale-specific features, for each city. Moreover, the convolutional architecture and the policy LSTM become general after training on a number of cities. So, new goal LSTMs are required to be trained in new cities.<br />
<br />
In this paper, the authors use IMPALA [1] to train the agents because IMPALA can get similar performance to A3C [2].<br />
<br />
===Prior on agent training: IMPALA and A3C===<br />
<br />
IMPALA (Importance Weighted Actor-Learner Architecture) is an actor-critic implementation of deep reinforcement learning that decouples actions from learning. IMPALA results in a comparable performance to A3C (Google DeepMind's previous algorithm: Asynchronous Actor-Critic Agents) on a single city task, but it has been shown to handle better multi-task learning than A3C. The authors use 256 actors for CityNav and 512 actors for MultiCityNav, with batch sizes of 256 or 512 respectively, and sequences are unrolled to length 50.<br />
<br />
===Curriculum Learning===<br />
In curriculum learning, the model is trained using simple examples in first steps. As soon as the model learns those examples, more complex and difficult examples would be fed to the model. In this paper, this approach is used to teach agent to navigate to further destinations. This courier task suffers from a common problem of RL tasks which is sparse rewards (similar to Montezuma’s Revenge) . To overcome this problem, a natural curriculum scheme is defined, in which sampling each new goal would be within 500m of the agent’s position. This is called phase 1. In phase 2, the maximum range is gradually increased to cover the full graph (3.5km in the smaller New York areas, or 5km for central London or Downtown Manhattan)<br />
<br />
Curriculum learning was first introduced by Bengio et. al in 2009. It serves as a continuation method for non-convex optimization, and improves training time by injecting noisy data. One example outside this paper for curriculum learning is outlined below:<br />
<br />
1. We aim to classify shapes within the following three classes: triangles, ellipses, and rectangles. We can create a curriculum by first starting with a simplified dataset that consists of only special cases of these three classes: equilateral triangles, circles, and squares. By first training on these special cases, and then introducing the full model, we can allow the algorithm to converge more quickly towards a local minima before providing "harder" examples. Feeding only these specialized examples also serves as a method to make the classes fall on more distinct manifold locations; with less overlap, these networks will perform better when noise is later added as well.<br />
<br />
==Results==<br />
In this section, the performance of the proposed architectures on the courier task is shown.<br />
<br />
[[File:figure5-2.png|600px|thumb|center|Figure 5. Average per-episode goal rewards (y-axis) are plotted vs. learning steps (x-axis) for the courier task in the NYU (New York City) environment (top), and in central London (bottom). We compare the GoalNav agent, the CityNav agent, and the CityNav agent without skip connection on the NYU environment, and the CityNav agent in London. We also compare the Oracle performance and a Heuristic agent, described below. The London agents were trained with a 2-phase curriculum– we indicate the end of phase 1 (500m only) and the end of phase 2 (500m to 5000m). Results on the Rive Gauche part of Paris (trained in the same way<br />
as in London) are comparable and the agent achieved mean goal reward 426.]]<br />
<br />
It is first shown that the CityNav agent, trained with curriculum learning, succeeds in learning the courier task in New York, London and Paris. Figure 5 compares the following agents:<br />
<br />
1. Goal Navigation agent.<br />
<br />
2. City Navigation Agent.<br />
<br />
3. A City Navigation agent without the skip connection from the vision layers to the policy LSTM. This is needed to regularise the interface between the goal LSTM and the policy LSTM in multi-city transfer scenario.<br />
<br />
Also, a lower bound (Heuristic) and an upper bound(Oracle) on the performance is considered. As it is said in the paper: "Heuristic is a random walk on the street graph, where the agent turns in a random direction if it cannot move forward; if at an intersection it will turn with a probability <math>P=0.95</math>. Oracle uses the full graph to compute the optimal path using breadth-first search.". As it is clear in Figure 5, CityNav architecture with the previously mentioned architecture attains a higher performance and is more stable than the simpler GoalNav agent.<br />
<br />
The trajectories of the trained agent over two 1000 step episodes and the value function of the agent during navigation to a destination is shown in Figure 6.<br />
<br />
[[File:figure6-soroush.png|400px|thumb|center|Figure 6. Trained CityNav agent’s performance in two environments: Central London (left panes), and NYU (right panes). Top: examples of the agent’s trajectory during one 1000-step episode, showing successful consecutive goal acquisitions. The arrows show the direction of travel of the agent. Bottom: We visualize the value function of the agent during 100 trajectories with random starting points and the same goal (respectively St Paul’s Cathedral and Washington Square). Thicker and warmer color lines correspond to higher value functions.]]<br />
<br />
Figure 7 shows that navigation policy is learned by agent successfully in St Paul’s Cathedral in London and Washington Square in New York.<br />
[[File:figure7-soroush.png|400px|thumb|center|Figure 7. Number of steps required for the CityNav agent to reach<br />
a goal (Washington Square in New York or St Paul’s Cathedral in<br />
London) from 100 start locations vs. the straight-line distance to<br />
the goal in meters. One agent step corresponds to a forward movement<br />
of about 10m or a left/right turn by 22.5 or 67.5 degrees.]]<br />
<br />
The authors mask 25% of the possible goals and train on the remaining ones in order to investigate the generalisation capability of a trained agent. Figure 8 Showa that the agent is still able to traverse through these areas, it just never samples a goal there. <br />
[[File:fff8.png|600px|center]]<br />
<br />
A critical test for this article is to transfer model to new cities by learning a new set of landmarks, but without re-learning visual representation, behaviors, etc. Therefore, the MultiCityNav agent is trained on a number of cities besides freezing both the policy LSTM and the convolutional encoder. Then a new locale-specific goal LSTM is trained. The performance is compared using three different training regimes, illustrated in Fig. 9: Training on only the target city (single training); training on multiple cities, including the target city, together (joint training); and joint training on all but the target city, followed by training on the target city with the rest of the architecture frozen (pre-train and transfer). Figure 10 shows that transferring to other cities is possible. Also, training the model on more cities would increase its effectiveness. According to the paper: "Remarkably, the agent that is pre-trained on 4 regions and then transferred to Wall Street achieves comparable performance to an agent trained jointly on all the regions, and only slightly worse than single-city training on Wall Street alone". Training the model in a single city using skip connection is useful. However, it is not useful in multi-city transferring.<br />
[[File:figure9-soroush.png|400px|thumb|center|Figure 9. Illustration of training regimes: (a) training on a single city (equivalent to CityNav); (b) joint training over multiple cities with a dedicated per-city pathway and shared convolutional net and policy LSTM; (c) joint pre-training on a number of cities followed by training on a target city with convolutional net and policy LSTM frozen (only the target city pathway is optimized).]]<br />
[[File:figure10-soroush.png|400px|thumb|center|Figure 10. Joint multi-city training and transfer learning performance of variants of the MultiCityNav agent evaluated only on the target city (Wall Street). We compare single-city training on the target environment alone vs. joint training on multiple cities (3, 4, or 5-way joint training including Wall Street), vs. pre-training on multiple cities and then transferring to Wall Street while freezing the entire agent except for the new pathway (see Fig. 10). One variant has skip connections between the convolutional encoder and the policy LSTM, the other does not (no-skip).]]<br />
<br />
Giving early rewards before agent reaches the goal or adding random rewards (coins) to encourage exploration is investigated in this article. Figure 11a suggests that coins by themselves are ineffective as our task does not benefit from wide explorations. Also, as it is clear from Figure 11b, reducing the density of the landmarks does not seem to reduce the performance. Based on the results, authors chose to start sampling the goal within a radius of 500m from the agent’s location, and then progressively extend it to the maximum distance an agent could travel within the environment. In addition, to asses the importance of the goal-conditioned agents, a Goal-less CityNav agent is trained by removing inputs gt. The poor performance of this agent is clear in Figure 11b. Furthermore, reducing the density of the landmarks by the ratio of 50%, 25%, and 12:5% does not reduce the performance that much. Finally, some alternative for goal representation is investigated:<br />
<br />
a) Latitude and longitude scalar coordinates normalized to be between 0 and 1. This is based on the region which the agent navigates.<br />
<br />
b) Binned representation. <br />
<br />
The latitude and longitude scalar goal representations perform the best. However, since the all landmarks representation performs well while remaining independent of the coordinate system, we use this representation as the canonical one.<br />
<br />
[[File:figure11-soroush.PNG|300px|thumb|center|Figure 11. Top: Learning curves of the CityNav agent on NYU, comparing reward shaping with different radii of early rewards (ER) vs. ER with random coins vs. curriculum learning with ER 200m and no coins (ER 200m, Curr.). Bottom: Learning curves for CityNav agents with different goal representations: landmark-based, as well as latitude and longitude classification-based and regression-based.]]<br />
<br />
==Conclusion==<br />
In this paper, a deep reinforcement learning approach that enables navigation in cities is presented through the use of Google StreetView for its photographic content and worldwide coverage. Furthermore, the authors discussed a new courier task and a multi-city neural network agent architecture that is transferable to new cities. A successful navigation architecture is presented which relies on integration of general policies with locale-specific knowledge.<br />
<br />
==Future Works==<br />
The paper uses staic Google Street View images. However, this means that there are some more information that we can get from the images beyond the route. Even though it is not the central focus of the paper, it would be extremely useful if we can incorporate such information for effective route-building or planning.<br />
<br />
[[File:picture1.png|400px|thumb|center|Figure 12 LearningcurvesoftheCityNavagent(2LSTM+Skip+HD) on NYU, comparing different ablations, allthey way down toGoalNav(LSTM). 2LSTM architectures havea global pathway LSTM and a policy LSTM with optional Skipconnection between the convnet and the policy LSTM. HD is theheading prediction auxiliary task.]]<br />
<br />
==Critique==<br />
1. It is not clear how this model is applicable to the real world. A real-world navigation problem needs to detect objects, people, and cars. However, it is not clear whether they are modeling them or not. From what I understood, they did not care about the collision, which is against their claim that it is a real-world problem.<br />
<br />
2. This paper is only using static Google Street View images as its primary source of data. But the authors must at least complement this with other dynamic data like traffic and road blockage information for a realistic model of navigation in the world. Also, this is quite understandable not to use maps but is not clear why have they not used GPS to know their position and maybe even made up with a map. This can be something useful in an emergency or even for investigating places that are not known or there is no access to them. The resulting map could be easily compared with the real one and could also be used in training to achieve higher performance. The availability should not be a serious problem because if they are simulating a real city and the google images are available, why should not GPS be? What is the intuition? At least, a complementary description on this could be helpful.<br />
<br />
3. The 'Transfer in Multi-City Experiments' results could be strengthened significantly via cross-validation (only Wall Street, which covers the smallest area of the four regions, is used as the test case). Additionally, the results do not show true 'multi-city' transfer learning, since all regions are within New York City. It is stated in the paper that not having to re-learn visual representations when transferring between cities is one of the outcomes, but the tests do not actually check for this. There are likely significant differences in the features that would be learned in NYC vs. Waterloo, for example, and this type of transfer has not been evaluated.<br />
<br />
4. The proposed navigation model could be limited by its reliance on pre-defined landmarks, which appears to be strategically placed evenly spreading across each city. This could limit the agent's deployability to new cities.<br />
<br />
==Reference==<br />
[1] Espeholt, Lasse, Soyer, Hubert, Munos, Remi, Simonyan, Karen, Mnih, Volodymir, Ward, Tom, Doron, Yotam, Firoiu, Vlad, Harley, Tim, Dunning, Iain, Legg, Shane, and Kavukcuoglu, Koray. Impala: Scalable distributed deep-rl with importance weighted actor-learner architec- tures. arXiv preprint arXiv:1802.01561, 2018.<br />
<br />
[2] Mnih, Volodymyr, Badia, Adria Puigdomenech, Mirza, Mehdi, Graves, Alex, Lillicrap, Timothy, Harley, Tim, Silver, David, and Kavukcuoglu, Koray. Asynchronous methods for deep reinforcement learning. In Interna- tional Conference on Machine Learning, pp. 1928–1937, 2016.</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Learning_to_Navigate_in_Cities_Without_a_Map&diff=42327Learning to Navigate in Cities Without a Map2018-12-08T01:10:48Z<p>Aghabuss: /* Future Works */</p>
<hr />
<div>Paper: <br />
[https://arxiv.org/pdf/1804.00168.pdf Learning to Navigate in Cities Without a Map]<br />
A video of the paper is available [https://sites.google.com/view/streetlearn here].<br />
<br />
== Introduction ==<br />
Navigation is an attractive topic in many research disciplines and technology related domains such as neuroscience and robotics. The majority of algorithms are based on the following steps.<br />
<br />
1. Building an explicit map<br />
<br />
2. Planning and acting using that map. <br />
<br />
In this article, based on this fact that human can learn to navigate through cities without using any special tool such as maps or GPS, authors propose new methods to show that a neural network agent can do the same thing by using visual observations. To do so, an interactive environment using Google StreetView Images and a dual pathway agent architecture is designed. As shown in figure 1, some parts of the environment are built using Google StreetView images of New York City (Times Square, Central Park) and London (St. Paul’s Cathedral). The green cone represents the agent’s location and orientation. Although learning to navigate using visual aids is shown to be successful in some domains such as games and simulated environments using deep reinforcement learning (RL), it suffers from data inefficiency and sensitivity to changes in the environment. Thus, it is unclear whether this method could be used for large-scale navigation. That’s why it became the subject of investigation in this paper.<br />
[[File:figure1-soroush.png|600px|thumb|center|Figure 1. Our environment is built of real-world places from StreetView. The figure shows diverse views and corresponding local maps (neither map nor current position have not been used by the agent) in New York City (Times Square, Central Park) and London (St. Paul’s Cathedral). The green cone represents the agent’s location and orientation.]]<br />
<br />
==Contribution==<br />
This paper has made the following contributions:<br />
<br />
1. Designing a dual pathway agent architecture. This agent can navigate through a real city and is trained with end-to-end reinforcement learning to handle real-world navigations.<br />
<br />
2. Using Goal-dependent learning. This means that the policy and value functions must adapt themselves to a sequence of goals that are provided as input.<br />
<br />
3. Leveraging a recurrent neural architecture. Using that, not only could navigation through a city be possible, but also the model is scalable for navigation in new cities. This architecture supports both locale-specific learnings and general transferable navigations. The authors achieved these by separating a recurrent neural pathway. This pathway receives and interprets the current goal as well as encapsulates and memorizes features of a single region.<br />
<br />
4. Using a new environment which is built on top of Google StreetView images. This provides real-world images for agent’s observation. Using this environment, the agent can navigate from an arbitrary starting point to a goal and then to another goal etc. Also, London, Paris, and New York City are chosen for navigation.<br />
<br />
The authors demonstrate that their proposed method can provide a mechanism for transferring knowledge to new cities. As with humans, when the agent visits a new city, the expectation is it to have it learn a new set of landmarks, but not to have to re-learn its visual representations or its behaviours (e.g., zooming forward along streets or turning at intersections). Therefore, using the MultiCity architecture, the paper trains first on a number of cities, then freezes both the policy network and the visual convolutional network and only a new locale-specific pathway on a new city. This approach enables the agent to acquire new knowledge without forgetting what it has already learned, similarly to the progressive neural networks architecture.<br />
<br />
==Related Work==<br />
<br />
1. Localization from real-world imagery. For example, (Weyand et al., 2016), a CNN was able to achieve excellent results on geolocation task. This paper provides novel work by not including supervised training with ground-truth labels, and by including planning as a goal. Some other works also improve by exploiting spatiotemporal continuity or estimating camera pose or depth estimation from pixels. These methods rely on supervised training with ground truth labels, which is not possible in every environment. <br />
<br />
2. Deep RL methods for navigation. For instance, (Mirowski et al., 2016; Jaderberg et al., 2016) used self-supervised auxiliary tasks to produce visual navigation in several created mazes. Some other researches used text descriptions to incorporate goal instructions. Researchers developed realistic, higher-fidelity environment simulations to make the experiment more realistic, but that still came with lack of diversities. This paper makes use of real-world data, in contrast to many related papers in this area. It's diverse and visually realistic but still, it does not contain dynamic elements, and the street topology cannot be regenerated or altered.<br />
<br />
3. Deep RL for path planning and mapping. For example, (Zhang et al., 2017) created an agent that represented a global map via an RL agent with external memory; some other work uses a hierarchical control strategy to propose a structured memory and Memory Augmented Control Maps. Explicit neural mapper and navigation planner with joint training was also used. Among all these works, the target-driven visual navigation with a goal-conditional policy approach was most related to our method.<br />
<br />
4. To make simulations resemble reality, researchers have developed higher-fidelity simulated environments (Dosovitskiy et al., 2017; Kolve et al., 2017; Shah et al., 2018; Wu et al., 2018). However, in spite of the photo-realism, the inherent problems of simulated environments pertain to the limited diversity of the environments and the idealistic cleanliness of the observations.<br />
<br />
==Environment==<br />
Google StreetView consists of both high-resolution 360-degree imagery and graph connectivity. Also, it provides a public API. These features make it a valuable resource. In this work, large areas of New York, Paris, and London that contain between 7,000 and 65,500 nodes<br />
(and between 7,200 and 128,600 edges, respectively), have a mean node spacing of 10m and cover a range of up to<br />
5km chosen (Figure 2), without simplifying the underlying connections. This means that there are many areas 'congested' with nodes, occlusions, available footpaths, etc. The agent only sees RGB images that are visible in StreetView images (Figure 1) and is not aware of the underlying graph.<br />
<br />
[[File:figure2-soroush.png|700px|thumb|center|Figure 2. Map of the 5 environments in New York City; our experiments focus on the NYU area as well as on transfer learning from the other areas to Wall Street (see Section 5.3). In the zoomed in area, each green dot corresponds to a unique panorama, the goal is marked in blue, and landmark locations are marked with red pins.]]<br />
<br />
==Agent Interface and the Courier Task==<br />
In an RL environment, we need to define observations and actions in addition to tasks. The inputs to the agent are the image <math>x_t</math> and the goal <math>g_t</math>. Also, a first-person view of the 3D environment is simulated by cropping <math>x_t</math> to a 60-degree square RGB image that is scaled to 84*84 pixels. Furthermore, the action space consists of 5 movements: “slow” rotate left or right (±22:5), “fast” rotate left or right (±67.5), or move forward (implemented as a ''noop'' in the case where this is not a viable action). The most central edge is chosen if there are multiple edges in the agents viewing cone.<br />
<br />
There are lots of ways to specify the goal to the agent. In this paper, the current goal is chosen to be represented in terms of its proximity to a set L of fixed landmarks <math> L={(Lat_k, Long_k)}</math> which are specified using Latitude and Longitude coordinate system. For distance to the <math> k_{th}</math> landmark <math>{(d_{(t,k)}^g})_k</math> the goal vector contains <math> g_{(t,i)}=\tfrac{exp(-αd_{(t,i)}^g)}{∑_k exp(-αd_{(t,k)}^g)} </math>for <math>i_{th}</math> landmark with <math>α=0.002</math> (Figure 3).<br />
<br />
[[File:figure3-soroush.PNG|400px|thumb|center|Figure 3. We illustrate the goal description by showing a goal and a set of 5 landmarks that are nearby, plus 4 that are more distant. The code <math>g_i</math> is a vector with a softmax-normalised distance to each landmark.]]<br />
<br />
This form of representation has several advantages: <br />
<br />
1. It could easily be extended to new environments.<br />
<br />
2. It is intuitive. Even humans and animals use landmarks to be able to move from one place to another.<br />
<br />
3. It does not rely on arbitrary map coordinates, and provides an absolute (as opposed to relative) goal.<br />
<br />
In this work, 644 landmarks for New York, Paris, and London are manually defined. The courier task is the problem of navigating to a list of random locations within a city. In each episode, which consists of 1000 steps, the agent starts from a random place with random orientation. when an agent gets within 100 meters of goal, the next goal is randomly chosen. An episode ends after 1000 agent steps. Finally, the reward is proportional to the shortest path between agent and goal when the goal is first assigned (providing more reward for longer journeys). Thus the agent needs to learn the mapping between the images observed at the goal location and the goal vector in order to solve the courier task problem. Furthermore, the agent must learn the association between the images observed at its current location and the policy to reach the goal destination.<br />
<br />
==Methods==<br />
<br />
===Goal-dependent Actor-Critic Reinforcement Learning===<br />
In this paper, the learning problem is based on Markov Decision Process, with state space <math>\mathcal{S}</math>, action space <math>\mathcal{A}</math>, environment <math>\mathcal{E}</math>, and a set of possible goals <math>\mathcal{G}</math>. The reward function depends on the current goal and state: <math>\mathcal{R}: \mathcal{S} \times \mathcal{G} \times \mathcal{A} &rarr; \mathbb{R}</math>. Typically, in reinforcement learning the main goal is to find the policy which maximizes the expected return. Expected return is defined as the sum of<br />
discounted rewards starting from state <math>s_0</math> with discount <math>\gamma</math>. Also, the expected return from a state <math>s_t</math> depends on the goals that are sampled. The policy is defined as a distribution over the actions, given the current state <math>s_t</math> and the goal <math>g_t</math>: <br />
<br />
\begin{align}<br />
\pi(\alpha|s,g)=Pr(\alpha_t=\alpha|s_t=s, g_t=g)<br />
\end{align}<br />
<br />
Value function is defined as the expected return obtained by sampling actions from policy <math>\pi</math> from state <math>s_t</math> with goal <math>g_t</math>:<br />
<br />
\begin{align}<br />
V^{\pi}(s,g)=E[R_t]=E[Σ_{k=0}^{\infty}\gamma^kr_{t+k}|s_t=s, g_t=g]<br />
\end{align}<br />
<br />
Also, an architecture with multiple pathways is designed to support two types of learning that is required for this problem. First, an agent needs an internal representation which is general and gives an understanding of a scene. Second, to better understand a scene the agent needs to remember unique features of the scene which then help the agent to organize and remember the scenes.<br />
<br />
===Architectures===<br />
<br />
[[File:figure4-soroush.png|400px|thumb|center|Figure 4. Comparison of architectures. Left: GoalNav is a convolutional encoder plus policy LSTM with goal description input. Middle: CityNav is a single-city navigation architecture with a separate goal LSTM and optional auxiliary heading (θ). Right: MultiCityNav is a multi-city architecture with individual goal LSTM pathways for each city.]]<br />
<br />
The authors use neural networks to parameterize policy and value functions. These neural networks share weights in all layers except the final linear layer. The agent takes image pixels as input. These pixels are passed through a convolutional network. The output of the Convolution network is fed to a Long Short-Term Memory (LSTM) as well as the past reward <math>r_{t-1}</math> and previous action <math>\alpha_{t-1}</math>.<br />
<br />
Three different architectures are described below.<br />
<br />
The '''GoalNav''' architecture (Fig. 4a) which consists of a convolutional architecture and policy LSTM. Goal description <math>g_t</math>, previous action, and reward are the inputs of this LSTM.<br />
<br />
The '''CityNav''' architecture (Fig. 4b) consists of the previous architecture alongside an additional LSTM, called the goal LSTM. Inputs of this LSTM are visual features and the goal description. The CityNav agent also adds an auxiliary heading (θ) prediction task which is defined as an angle between the north direction and the agent’s pose. This auxiliary task can speed up learning and provides relevant information. <br />
<br />
The '''MultiCityNav''' architecture (Fig. 4c) is an extension of CityNav for learning in different cities. This is done using the parallel connection of goal LSTMs for encapsulating locale-specific features, for each city. Moreover, the convolutional architecture and the policy LSTM become general after training on a number of cities. So, new goal LSTMs are required to be trained in new cities.<br />
<br />
In this paper, the authors use IMPALA [1] to train the agents because IMPALA can get similar performance to A3C [2].<br />
<br />
===Prior on agent training: IMPALA and A3C===<br />
<br />
IMPALA (Importance Weighted Actor-Learner Architecture) is an actor-critic implementation of deep reinforcement learning that decouples actions from learning. IMPALA results in a comparable performance to A3C (Google DeepMind's previous algorithm: Asynchronous Actor-Critic Agents) on a single city task, but it has been shown to handle better multi-task learning than A3C. The authors use 256 actors for CityNav and 512 actors for MultiCityNav, with batch sizes of 256 or 512 respectively, and sequences are unrolled to length 50.<br />
<br />
===Curriculum Learning===<br />
In curriculum learning, the model is trained using simple examples in first steps. As soon as the model learns those examples, more complex and difficult examples would be fed to the model. In this paper, this approach is used to teach agent to navigate to further destinations. This courier task suffers from a common problem of RL tasks which is sparse rewards (similar to Montezuma’s Revenge) . To overcome this problem, a natural curriculum scheme is defined, in which sampling each new goal would be within 500m of the agent’s position. This is called phase 1. In phase 2, the maximum range is gradually increased to cover the full graph (3.5km in the smaller New York areas, or 5km for central London or Downtown Manhattan)<br />
<br />
Curriculum learning was first introduced by Bengio et. al in 2009. It serves as a continuation method for non-convex optimization, and improves training time by injecting noisy data. One example outside this paper for curriculum learning is outlined below:<br />
<br />
1. We aim to classify shapes within the following three classes: triangles, ellipses, and rectangles. We can create a curriculum by first starting with a simplified dataset that consists of only special cases of these three classes: equilateral triangles, circles, and squares. By first training on these special cases, and then introducing the full model, we can allow the algorithm to converge more quickly towards a local minima before providing "harder" examples. Feeding only these specialized examples also serves as a method to make the classes fall on more distinct manifold locations; with less overlap, these networks will perform better when noise is later added as well.<br />
<br />
==Results==<br />
In this section, the performance of the proposed architectures on the courier task is shown.<br />
<br />
[[File:figure5-2.png|600px|thumb|center|Figure 5. Average per-episode goal rewards (y-axis) are plotted vs. learning steps (x-axis) for the courier task in the NYU (New York City) environment (top), and in central London (bottom). We compare the GoalNav agent, the CityNav agent, and the CityNav agent without skip connection on the NYU environment, and the CityNav agent in London. We also compare the Oracle performance and a Heuristic agent, described below. The London agents were trained with a 2-phase curriculum– we indicate the end of phase 1 (500m only) and the end of phase 2 (500m to 5000m). Results on the Rive Gauche part of Paris (trained in the same way<br />
as in London) are comparable and the agent achieved mean goal reward 426.]]<br />
<br />
It is first shown that the CityNav agent, trained with curriculum learning, succeeds in learning the courier task in New York, London and Paris. Figure 5 compares the following agents:<br />
<br />
1. Goal Navigation agent.<br />
<br />
2. City Navigation Agent.<br />
<br />
3. A City Navigation agent without the skip connection from the vision layers to the policy LSTM. This is needed to regularise the interface between the goal LSTM and the policy LSTM in multi-city transfer scenario.<br />
<br />
Also, a lower bound (Heuristic) and an upper bound(Oracle) on the performance is considered. As it is said in the paper: "Heuristic is a random walk on the street graph, where the agent turns in a random direction if it cannot move forward; if at an intersection it will turn with a probability <math>P=0.95</math>. Oracle uses the full graph to compute the optimal path using breadth-first search.". As it is clear in Figure 5, CityNav architecture with the previously mentioned architecture attains a higher performance and is more stable than the simpler GoalNav agent.<br />
<br />
The trajectories of the trained agent over two 1000 step episodes and the value function of the agent during navigation to a destination is shown in Figure 6.<br />
<br />
[[File:figure6-soroush.png|400px|thumb|center|Figure 6. Trained CityNav agent’s performance in two environments: Central London (left panes), and NYU (right panes). Top: examples of the agent’s trajectory during one 1000-step episode, showing successful consecutive goal acquisitions. The arrows show the direction of travel of the agent. Bottom: We visualize the value function of the agent during 100 trajectories with random starting points and the same goal (respectively St Paul’s Cathedral and Washington Square). Thicker and warmer color lines correspond to higher value functions.]]<br />
<br />
Figure 7 shows that navigation policy is learned by agent successfully in St Paul’s Cathedral in London and Washington Square in New York.<br />
[[File:figure7-soroush.png|400px|thumb|center|Figure 7. Number of steps required for the CityNav agent to reach<br />
a goal (Washington Square in New York or St Paul’s Cathedral in<br />
London) from 100 start locations vs. the straight-line distance to<br />
the goal in meters. One agent step corresponds to a forward movement<br />
of about 10m or a left/right turn by 22.5 or 67.5 degrees.]]<br />
<br />
The authors mask 25% of the possible goals and train on the remaining ones in order to investigate the generalisation capability of a trained agent. Figure 8 Showa that the agent is still able to traverse through these areas, it just never samples a goal there. <br />
[[File:fff8.png|600px|center]]<br />
<br />
A critical test for this article is to transfer model to new cities by learning a new set of landmarks, but without re-learning visual representation, behaviors, etc. Therefore, the MultiCityNav agent is trained on a number of cities besides freezing both the policy LSTM and the convolutional encoder. Then a new locale-specific goal LSTM is trained. The performance is compared using three different training regimes, illustrated in Fig. 9: Training on only the target city (single training); training on multiple cities, including the target city, together (joint training); and joint training on all but the target city, followed by training on the target city with the rest of the architecture frozen (pre-train and transfer). Figure 10 shows that transferring to other cities is possible. Also, training the model on more cities would increase its effectiveness. According to the paper: "Remarkably, the agent that is pre-trained on 4 regions and then transferred to Wall Street achieves comparable performance to an agent trained jointly on all the regions, and only slightly worse than single-city training on Wall Street alone". Training the model in a single city using skip connection is useful. However, it is not useful in multi-city transferring.<br />
[[File:figure9-soroush.png|400px|thumb|center|Figure 9. Illustration of training regimes: (a) training on a single city (equivalent to CityNav); (b) joint training over multiple cities with a dedicated per-city pathway and shared convolutional net and policy LSTM; (c) joint pre-training on a number of cities followed by training on a target city with convolutional net and policy LSTM frozen (only the target city pathway is optimized).]]<br />
[[File:figure10-soroush.png|400px|thumb|center|Figure 10. Joint multi-city training and transfer learning performance of variants of the MultiCityNav agent evaluated only on the target city (Wall Street). We compare single-city training on the target environment alone vs. joint training on multiple cities (3, 4, or 5-way joint training including Wall Street), vs. pre-training on multiple cities and then transferring to Wall Street while freezing the entire agent except for the new pathway (see Fig. 10). One variant has skip connections between the convolutional encoder and the policy LSTM, the other does not (no-skip).]]<br />
<br />
Giving early rewards before agent reaches the goal or adding random rewards (coins) to encourage exploration is investigated in this article. Figure 11a suggests that coins by themselves are ineffective as our task does not benefit from wide explorations. Also, as it is clear from Figure 11b, reducing the density of the landmarks does not seem to reduce the performance. Based on the results, authors chose to start sampling the goal within a radius of 500m from the agent’s location, and then progressively extend it to the maximum distance an agent could travel within the environment. In addition, to asses the importance of the goal-conditioned agents, a Goal-less CityNav agent is trained by removing inputs gt. The poor performance of this agent is clear in Figure 11b. Furthermore, reducing the density of the landmarks by the ratio of 50%, 25%, and 12:5% does not reduce the performance that much. Finally, some alternative for goal representation is investigated:<br />
<br />
a) Latitude and longitude scalar coordinates normalized to be between 0 and 1. This is based on the region which the agent navigates.<br />
<br />
b) Binned representation. <br />
<br />
The latitude and longitude scalar goal representations perform the best. However, since the all landmarks representation performs well while remaining independent of the coordinate system, we use this representation as the canonical one.<br />
<br />
[[File:figure11-soroush.PNG|300px|thumb|center|Figure 11. Top: Learning curves of the CityNav agent on NYU, comparing reward shaping with different radii of early rewards (ER) vs. ER with random coins vs. curriculum learning with ER 200m and no coins (ER 200m, Curr.). Bottom: Learning curves for CityNav agents with different goal representations: landmark-based, as well as latitude and longitude classification-based and regression-based.]]<br />
<br />
==Conclusion==<br />
In this paper, a deep reinforcement learning approach that enables navigation in cities is presented through the use of Google StreetView for its photographic content and worldwide coverage. Furthermore, the authors discussed a new courier task and a multi-city neural network agent architecture that is transferable to new cities. A successful navigation architecture is presented which relies on integration of general policies with locale-specific knowledge.<br />
<br />
==Future Works==<br />
The paper uses staic Google Street View images. However, this means that there are some more information that we can get from the images beyond the route. Even though it is not the central focus of the paper, it would be extremely useful if we can incorporate such information for effective route-building or planning.<br />
<br />
[[File:picture1.png|400px|center|Figure 8. LearningcurvesoftheCityNavagent(2LSTM+Skip+HD) on NYU, comparing different ablations, allthey way down toGoalNav(LSTM). 2LSTM architectures havea global pathway LSTM and a policy LSTM with optional Skipconnection between the convnet and the policy LSTM. HD is theheading prediction auxiliary task.]]<br />
<br />
==Critique==<br />
1. It is not clear how this model is applicable to the real world. A real-world navigation problem needs to detect objects, people, and cars. However, it is not clear whether they are modeling them or not. From what I understood, they did not care about the collision, which is against their claim that it is a real-world problem.<br />
<br />
2. This paper is only using static Google Street View images as its primary source of data. But the authors must at least complement this with other dynamic data like traffic and road blockage information for a realistic model of navigation in the world. Also, this is quite understandable not to use maps but is not clear why have they not used GPS to know their position and maybe even made up with a map. This can be something useful in an emergency or even for investigating places that are not known or there is no access to them. The resulting map could be easily compared with the real one and could also be used in training to achieve higher performance. The availability should not be a serious problem because if they are simulating a real city and the google images are available, why should not GPS be? What is the intuition? At least, a complementary description on this could be helpful.<br />
<br />
3. The 'Transfer in Multi-City Experiments' results could be strengthened significantly via cross-validation (only Wall Street, which covers the smallest area of the four regions, is used as the test case). Additionally, the results do not show true 'multi-city' transfer learning, since all regions are within New York City. It is stated in the paper that not having to re-learn visual representations when transferring between cities is one of the outcomes, but the tests do not actually check for this. There are likely significant differences in the features that would be learned in NYC vs. Waterloo, for example, and this type of transfer has not been evaluated.<br />
<br />
4. The proposed navigation model could be limited by its reliance on pre-defined landmarks, which appears to be strategically placed evenly spreading across each city. This could limit the agent's deployability to new cities.<br />
<br />
==Reference==<br />
[1] Espeholt, Lasse, Soyer, Hubert, Munos, Remi, Simonyan, Karen, Mnih, Volodymir, Ward, Tom, Doron, Yotam, Firoiu, Vlad, Harley, Tim, Dunning, Iain, Legg, Shane, and Kavukcuoglu, Koray. Impala: Scalable distributed deep-rl with importance weighted actor-learner architec- tures. arXiv preprint arXiv:1802.01561, 2018.<br />
<br />
[2] Mnih, Volodymyr, Badia, Adria Puigdomenech, Mirza, Mehdi, Graves, Alex, Lillicrap, Timothy, Harley, Tim, Silver, David, and Kavukcuoglu, Koray. Asynchronous methods for deep reinforcement learning. In Interna- tional Conference on Machine Learning, pp. 1928–1937, 2016.</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Learning_to_Navigate_in_Cities_Without_a_Map&diff=42326Learning to Navigate in Cities Without a Map2018-12-08T01:09:48Z<p>Aghabuss: /* Future Works */</p>
<hr />
<div>Paper: <br />
[https://arxiv.org/pdf/1804.00168.pdf Learning to Navigate in Cities Without a Map]<br />
A video of the paper is available [https://sites.google.com/view/streetlearn here].<br />
<br />
== Introduction ==<br />
Navigation is an attractive topic in many research disciplines and technology related domains such as neuroscience and robotics. The majority of algorithms are based on the following steps.<br />
<br />
1. Building an explicit map<br />
<br />
2. Planning and acting using that map. <br />
<br />
In this article, based on this fact that human can learn to navigate through cities without using any special tool such as maps or GPS, authors propose new methods to show that a neural network agent can do the same thing by using visual observations. To do so, an interactive environment using Google StreetView Images and a dual pathway agent architecture is designed. As shown in figure 1, some parts of the environment are built using Google StreetView images of New York City (Times Square, Central Park) and London (St. Paul’s Cathedral). The green cone represents the agent’s location and orientation. Although learning to navigate using visual aids is shown to be successful in some domains such as games and simulated environments using deep reinforcement learning (RL), it suffers from data inefficiency and sensitivity to changes in the environment. Thus, it is unclear whether this method could be used for large-scale navigation. That’s why it became the subject of investigation in this paper.<br />
[[File:figure1-soroush.png|600px|thumb|center|Figure 1. Our environment is built of real-world places from StreetView. The figure shows diverse views and corresponding local maps (neither map nor current position have not been used by the agent) in New York City (Times Square, Central Park) and London (St. Paul’s Cathedral). The green cone represents the agent’s location and orientation.]]<br />
<br />
==Contribution==<br />
This paper has made the following contributions:<br />
<br />
1. Designing a dual pathway agent architecture. This agent can navigate through a real city and is trained with end-to-end reinforcement learning to handle real-world navigations.<br />
<br />
2. Using Goal-dependent learning. This means that the policy and value functions must adapt themselves to a sequence of goals that are provided as input.<br />
<br />
3. Leveraging a recurrent neural architecture. Using that, not only could navigation through a city be possible, but also the model is scalable for navigation in new cities. This architecture supports both locale-specific learnings and general transferable navigations. The authors achieved these by separating a recurrent neural pathway. This pathway receives and interprets the current goal as well as encapsulates and memorizes features of a single region.<br />
<br />
4. Using a new environment which is built on top of Google StreetView images. This provides real-world images for agent’s observation. Using this environment, the agent can navigate from an arbitrary starting point to a goal and then to another goal etc. Also, London, Paris, and New York City are chosen for navigation.<br />
<br />
The authors demonstrate that their proposed method can provide a mechanism for transferring knowledge to new cities. As with humans, when the agent visits a new city, the expectation is it to have it learn a new set of landmarks, but not to have to re-learn its visual representations or its behaviours (e.g., zooming forward along streets or turning at intersections). Therefore, using the MultiCity architecture, the paper trains first on a number of cities, then freezes both the policy network and the visual convolutional network and only a new locale-specific pathway on a new city. This approach enables the agent to acquire new knowledge without forgetting what it has already learned, similarly to the progressive neural networks architecture.<br />
<br />
==Related Work==<br />
<br />
1. Localization from real-world imagery. For example, (Weyand et al., 2016), a CNN was able to achieve excellent results on geolocation task. This paper provides novel work by not including supervised training with ground-truth labels, and by including planning as a goal. Some other works also improve by exploiting spatiotemporal continuity or estimating camera pose or depth estimation from pixels. These methods rely on supervised training with ground truth labels, which is not possible in every environment. <br />
<br />
2. Deep RL methods for navigation. For instance, (Mirowski et al., 2016; Jaderberg et al., 2016) used self-supervised auxiliary tasks to produce visual navigation in several created mazes. Some other researches used text descriptions to incorporate goal instructions. Researchers developed realistic, higher-fidelity environment simulations to make the experiment more realistic, but that still came with lack of diversities. This paper makes use of real-world data, in contrast to many related papers in this area. It's diverse and visually realistic but still, it does not contain dynamic elements, and the street topology cannot be regenerated or altered.<br />
<br />
3. Deep RL for path planning and mapping. For example, (Zhang et al., 2017) created an agent that represented a global map via an RL agent with external memory; some other work uses a hierarchical control strategy to propose a structured memory and Memory Augmented Control Maps. Explicit neural mapper and navigation planner with joint training was also used. Among all these works, the target-driven visual navigation with a goal-conditional policy approach was most related to our method.<br />
<br />
4. To make simulations resemble reality, researchers have developed higher-fidelity simulated environments (Dosovitskiy et al., 2017; Kolve et al., 2017; Shah et al., 2018; Wu et al., 2018). However, in spite of the photo-realism, the inherent problems of simulated environments pertain to the limited diversity of the environments and the idealistic cleanliness of the observations.<br />
<br />
==Environment==<br />
Google StreetView consists of both high-resolution 360-degree imagery and graph connectivity. Also, it provides a public API. These features make it a valuable resource. In this work, large areas of New York, Paris, and London that contain between 7,000 and 65,500 nodes<br />
(and between 7,200 and 128,600 edges, respectively), have a mean node spacing of 10m and cover a range of up to<br />
5km chosen (Figure 2), without simplifying the underlying connections. This means that there are many areas 'congested' with nodes, occlusions, available footpaths, etc. The agent only sees RGB images that are visible in StreetView images (Figure 1) and is not aware of the underlying graph.<br />
<br />
[[File:figure2-soroush.png|700px|thumb|center|Figure 2. Map of the 5 environments in New York City; our experiments focus on the NYU area as well as on transfer learning from the other areas to Wall Street (see Section 5.3). In the zoomed in area, each green dot corresponds to a unique panorama, the goal is marked in blue, and landmark locations are marked with red pins.]]<br />
<br />
==Agent Interface and the Courier Task==<br />
In an RL environment, we need to define observations and actions in addition to tasks. The inputs to the agent are the image <math>x_t</math> and the goal <math>g_t</math>. Also, a first-person view of the 3D environment is simulated by cropping <math>x_t</math> to a 60-degree square RGB image that is scaled to 84*84 pixels. Furthermore, the action space consists of 5 movements: “slow” rotate left or right (±22:5), “fast” rotate left or right (±67.5), or move forward (implemented as a ''noop'' in the case where this is not a viable action). The most central edge is chosen if there are multiple edges in the agents viewing cone.<br />
<br />
There are lots of ways to specify the goal to the agent. In this paper, the current goal is chosen to be represented in terms of its proximity to a set L of fixed landmarks <math> L={(Lat_k, Long_k)}</math> which are specified using Latitude and Longitude coordinate system. For distance to the <math> k_{th}</math> landmark <math>{(d_{(t,k)}^g})_k</math> the goal vector contains <math> g_{(t,i)}=\tfrac{exp(-αd_{(t,i)}^g)}{∑_k exp(-αd_{(t,k)}^g)} </math>for <math>i_{th}</math> landmark with <math>α=0.002</math> (Figure 3).<br />
<br />
[[File:figure3-soroush.PNG|400px|thumb|center|Figure 3. We illustrate the goal description by showing a goal and a set of 5 landmarks that are nearby, plus 4 that are more distant. The code <math>g_i</math> is a vector with a softmax-normalised distance to each landmark.]]<br />
<br />
This form of representation has several advantages: <br />
<br />
1. It could easily be extended to new environments.<br />
<br />
2. It is intuitive. Even humans and animals use landmarks to be able to move from one place to another.<br />
<br />
3. It does not rely on arbitrary map coordinates, and provides an absolute (as opposed to relative) goal.<br />
<br />
In this work, 644 landmarks for New York, Paris, and London are manually defined. The courier task is the problem of navigating to a list of random locations within a city. In each episode, which consists of 1000 steps, the agent starts from a random place with random orientation. when an agent gets within 100 meters of goal, the next goal is randomly chosen. An episode ends after 1000 agent steps. Finally, the reward is proportional to the shortest path between agent and goal when the goal is first assigned (providing more reward for longer journeys). Thus the agent needs to learn the mapping between the images observed at the goal location and the goal vector in order to solve the courier task problem. Furthermore, the agent must learn the association between the images observed at its current location and the policy to reach the goal destination.<br />
<br />
==Methods==<br />
<br />
===Goal-dependent Actor-Critic Reinforcement Learning===<br />
In this paper, the learning problem is based on Markov Decision Process, with state space <math>\mathcal{S}</math>, action space <math>\mathcal{A}</math>, environment <math>\mathcal{E}</math>, and a set of possible goals <math>\mathcal{G}</math>. The reward function depends on the current goal and state: <math>\mathcal{R}: \mathcal{S} \times \mathcal{G} \times \mathcal{A} &rarr; \mathbb{R}</math>. Typically, in reinforcement learning the main goal is to find the policy which maximizes the expected return. Expected return is defined as the sum of<br />
discounted rewards starting from state <math>s_0</math> with discount <math>\gamma</math>. Also, the expected return from a state <math>s_t</math> depends on the goals that are sampled. The policy is defined as a distribution over the actions, given the current state <math>s_t</math> and the goal <math>g_t</math>: <br />
<br />
\begin{align}<br />
\pi(\alpha|s,g)=Pr(\alpha_t=\alpha|s_t=s, g_t=g)<br />
\end{align}<br />
<br />
Value function is defined as the expected return obtained by sampling actions from policy <math>\pi</math> from state <math>s_t</math> with goal <math>g_t</math>:<br />
<br />
\begin{align}<br />
V^{\pi}(s,g)=E[R_t]=E[Σ_{k=0}^{\infty}\gamma^kr_{t+k}|s_t=s, g_t=g]<br />
\end{align}<br />
<br />
Also, an architecture with multiple pathways is designed to support two types of learning that is required for this problem. First, an agent needs an internal representation which is general and gives an understanding of a scene. Second, to better understand a scene the agent needs to remember unique features of the scene which then help the agent to organize and remember the scenes.<br />
<br />
===Architectures===<br />
<br />
[[File:figure4-soroush.png|400px|thumb|center|Figure 4. Comparison of architectures. Left: GoalNav is a convolutional encoder plus policy LSTM with goal description input. Middle: CityNav is a single-city navigation architecture with a separate goal LSTM and optional auxiliary heading (θ). Right: MultiCityNav is a multi-city architecture with individual goal LSTM pathways for each city.]]<br />
<br />
The authors use neural networks to parameterize policy and value functions. These neural networks share weights in all layers except the final linear layer. The agent takes image pixels as input. These pixels are passed through a convolutional network. The output of the Convolution network is fed to a Long Short-Term Memory (LSTM) as well as the past reward <math>r_{t-1}</math> and previous action <math>\alpha_{t-1}</math>.<br />
<br />
Three different architectures are described below.<br />
<br />
The '''GoalNav''' architecture (Fig. 4a) which consists of a convolutional architecture and policy LSTM. Goal description <math>g_t</math>, previous action, and reward are the inputs of this LSTM.<br />
<br />
The '''CityNav''' architecture (Fig. 4b) consists of the previous architecture alongside an additional LSTM, called the goal LSTM. Inputs of this LSTM are visual features and the goal description. The CityNav agent also adds an auxiliary heading (θ) prediction task which is defined as an angle between the north direction and the agent’s pose. This auxiliary task can speed up learning and provides relevant information. <br />
<br />
The '''MultiCityNav''' architecture (Fig. 4c) is an extension of CityNav for learning in different cities. This is done using the parallel connection of goal LSTMs for encapsulating locale-specific features, for each city. Moreover, the convolutional architecture and the policy LSTM become general after training on a number of cities. So, new goal LSTMs are required to be trained in new cities.<br />
<br />
In this paper, the authors use IMPALA [1] to train the agents because IMPALA can get similar performance to A3C [2].<br />
<br />
===Prior on agent training: IMPALA and A3C===<br />
<br />
IMPALA (Importance Weighted Actor-Learner Architecture) is an actor-critic implementation of deep reinforcement learning that decouples actions from learning. IMPALA results in a comparable performance to A3C (Google DeepMind's previous algorithm: Asynchronous Actor-Critic Agents) on a single city task, but it has been shown to handle better multi-task learning than A3C. The authors use 256 actors for CityNav and 512 actors for MultiCityNav, with batch sizes of 256 or 512 respectively, and sequences are unrolled to length 50.<br />
<br />
===Curriculum Learning===<br />
In curriculum learning, the model is trained using simple examples in first steps. As soon as the model learns those examples, more complex and difficult examples would be fed to the model. In this paper, this approach is used to teach agent to navigate to further destinations. This courier task suffers from a common problem of RL tasks which is sparse rewards (similar to Montezuma’s Revenge) . To overcome this problem, a natural curriculum scheme is defined, in which sampling each new goal would be within 500m of the agent’s position. This is called phase 1. In phase 2, the maximum range is gradually increased to cover the full graph (3.5km in the smaller New York areas, or 5km for central London or Downtown Manhattan)<br />
<br />
Curriculum learning was first introduced by Bengio et. al in 2009. It serves as a continuation method for non-convex optimization, and improves training time by injecting noisy data. One example outside this paper for curriculum learning is outlined below:<br />
<br />
1. We aim to classify shapes within the following three classes: triangles, ellipses, and rectangles. We can create a curriculum by first starting with a simplified dataset that consists of only special cases of these three classes: equilateral triangles, circles, and squares. By first training on these special cases, and then introducing the full model, we can allow the algorithm to converge more quickly towards a local minima before providing "harder" examples. Feeding only these specialized examples also serves as a method to make the classes fall on more distinct manifold locations; with less overlap, these networks will perform better when noise is later added as well.<br />
<br />
==Results==<br />
In this section, the performance of the proposed architectures on the courier task is shown.<br />
<br />
[[File:figure5-2.png|600px|thumb|center|Figure 5. Average per-episode goal rewards (y-axis) are plotted vs. learning steps (x-axis) for the courier task in the NYU (New York City) environment (top), and in central London (bottom). We compare the GoalNav agent, the CityNav agent, and the CityNav agent without skip connection on the NYU environment, and the CityNav agent in London. We also compare the Oracle performance and a Heuristic agent, described below. The London agents were trained with a 2-phase curriculum– we indicate the end of phase 1 (500m only) and the end of phase 2 (500m to 5000m). Results on the Rive Gauche part of Paris (trained in the same way<br />
as in London) are comparable and the agent achieved mean goal reward 426.]]<br />
<br />
It is first shown that the CityNav agent, trained with curriculum learning, succeeds in learning the courier task in New York, London and Paris. Figure 5 compares the following agents:<br />
<br />
1. Goal Navigation agent.<br />
<br />
2. City Navigation Agent.<br />
<br />
3. A City Navigation agent without the skip connection from the vision layers to the policy LSTM. This is needed to regularise the interface between the goal LSTM and the policy LSTM in multi-city transfer scenario.<br />
<br />
Also, a lower bound (Heuristic) and an upper bound(Oracle) on the performance is considered. As it is said in the paper: "Heuristic is a random walk on the street graph, where the agent turns in a random direction if it cannot move forward; if at an intersection it will turn with a probability <math>P=0.95</math>. Oracle uses the full graph to compute the optimal path using breadth-first search.". As it is clear in Figure 5, CityNav architecture with the previously mentioned architecture attains a higher performance and is more stable than the simpler GoalNav agent.<br />
<br />
The trajectories of the trained agent over two 1000 step episodes and the value function of the agent during navigation to a destination is shown in Figure 6.<br />
<br />
[[File:figure6-soroush.png|400px|thumb|center|Figure 6. Trained CityNav agent’s performance in two environments: Central London (left panes), and NYU (right panes). Top: examples of the agent’s trajectory during one 1000-step episode, showing successful consecutive goal acquisitions. The arrows show the direction of travel of the agent. Bottom: We visualize the value function of the agent during 100 trajectories with random starting points and the same goal (respectively St Paul’s Cathedral and Washington Square). Thicker and warmer color lines correspond to higher value functions.]]<br />
<br />
Figure 7 shows that navigation policy is learned by agent successfully in St Paul’s Cathedral in London and Washington Square in New York.<br />
[[File:figure7-soroush.png|400px|thumb|center|Figure 7. Number of steps required for the CityNav agent to reach<br />
a goal (Washington Square in New York or St Paul’s Cathedral in<br />
London) from 100 start locations vs. the straight-line distance to<br />
the goal in meters. One agent step corresponds to a forward movement<br />
of about 10m or a left/right turn by 22.5 or 67.5 degrees.]]<br />
<br />
The authors mask 25% of the possible goals and train on the remaining ones in order to investigate the generalisation capability of a trained agent. Figure 8 Showa that the agent is still able to traverse through these areas, it just never samples a goal there. <br />
[[File:fff8.png|600px|center]]<br />
<br />
A critical test for this article is to transfer model to new cities by learning a new set of landmarks, but without re-learning visual representation, behaviors, etc. Therefore, the MultiCityNav agent is trained on a number of cities besides freezing both the policy LSTM and the convolutional encoder. Then a new locale-specific goal LSTM is trained. The performance is compared using three different training regimes, illustrated in Fig. 9: Training on only the target city (single training); training on multiple cities, including the target city, together (joint training); and joint training on all but the target city, followed by training on the target city with the rest of the architecture frozen (pre-train and transfer). Figure 10 shows that transferring to other cities is possible. Also, training the model on more cities would increase its effectiveness. According to the paper: "Remarkably, the agent that is pre-trained on 4 regions and then transferred to Wall Street achieves comparable performance to an agent trained jointly on all the regions, and only slightly worse than single-city training on Wall Street alone". Training the model in a single city using skip connection is useful. However, it is not useful in multi-city transferring.<br />
[[File:figure9-soroush.png|400px|thumb|center|Figure 9. Illustration of training regimes: (a) training on a single city (equivalent to CityNav); (b) joint training over multiple cities with a dedicated per-city pathway and shared convolutional net and policy LSTM; (c) joint pre-training on a number of cities followed by training on a target city with convolutional net and policy LSTM frozen (only the target city pathway is optimized).]]<br />
[[File:figure10-soroush.png|400px|thumb|center|Figure 10. Joint multi-city training and transfer learning performance of variants of the MultiCityNav agent evaluated only on the target city (Wall Street). We compare single-city training on the target environment alone vs. joint training on multiple cities (3, 4, or 5-way joint training including Wall Street), vs. pre-training on multiple cities and then transferring to Wall Street while freezing the entire agent except for the new pathway (see Fig. 10). One variant has skip connections between the convolutional encoder and the policy LSTM, the other does not (no-skip).]]<br />
<br />
Giving early rewards before agent reaches the goal or adding random rewards (coins) to encourage exploration is investigated in this article. Figure 11a suggests that coins by themselves are ineffective as our task does not benefit from wide explorations. Also, as it is clear from Figure 11b, reducing the density of the landmarks does not seem to reduce the performance. Based on the results, authors chose to start sampling the goal within a radius of 500m from the agent’s location, and then progressively extend it to the maximum distance an agent could travel within the environment. In addition, to asses the importance of the goal-conditioned agents, a Goal-less CityNav agent is trained by removing inputs gt. The poor performance of this agent is clear in Figure 11b. Furthermore, reducing the density of the landmarks by the ratio of 50%, 25%, and 12:5% does not reduce the performance that much. Finally, some alternative for goal representation is investigated:<br />
<br />
a) Latitude and longitude scalar coordinates normalized to be between 0 and 1. This is based on the region which the agent navigates.<br />
<br />
b) Binned representation. <br />
<br />
The latitude and longitude scalar goal representations perform the best. However, since the all landmarks representation performs well while remaining independent of the coordinate system, we use this representation as the canonical one.<br />
<br />
[[File:figure11-soroush.PNG|300px|thumb|center|Figure 11. Top: Learning curves of the CityNav agent on NYU, comparing reward shaping with different radii of early rewards (ER) vs. ER with random coins vs. curriculum learning with ER 200m and no coins (ER 200m, Curr.). Bottom: Learning curves for CityNav agents with different goal representations: landmark-based, as well as latitude and longitude classification-based and regression-based.]]<br />
<br />
==Conclusion==<br />
In this paper, a deep reinforcement learning approach that enables navigation in cities is presented through the use of Google StreetView for its photographic content and worldwide coverage. Furthermore, the authors discussed a new courier task and a multi-city neural network agent architecture that is transferable to new cities. A successful navigation architecture is presented which relies on integration of general policies with locale-specific knowledge.<br />
<br />
==Future Works==<br />
The paper uses staic Google Street View images. However, this means that there are some more information that we can get from the images beyond the route. Even though it is not the central focus of the paper, it would be extremely useful if we can incorporate such information for effective route-building or planning.<br />
<br />
[[File:picture1.png|400px|center]]<br />
<br />
==Critique==<br />
1. It is not clear how this model is applicable to the real world. A real-world navigation problem needs to detect objects, people, and cars. However, it is not clear whether they are modeling them or not. From what I understood, they did not care about the collision, which is against their claim that it is a real-world problem.<br />
<br />
2. This paper is only using static Google Street View images as its primary source of data. But the authors must at least complement this with other dynamic data like traffic and road blockage information for a realistic model of navigation in the world. Also, this is quite understandable not to use maps but is not clear why have they not used GPS to know their position and maybe even made up with a map. This can be something useful in an emergency or even for investigating places that are not known or there is no access to them. The resulting map could be easily compared with the real one and could also be used in training to achieve higher performance. The availability should not be a serious problem because if they are simulating a real city and the google images are available, why should not GPS be? What is the intuition? At least, a complementary description on this could be helpful.<br />
<br />
3. The 'Transfer in Multi-City Experiments' results could be strengthened significantly via cross-validation (only Wall Street, which covers the smallest area of the four regions, is used as the test case). Additionally, the results do not show true 'multi-city' transfer learning, since all regions are within New York City. It is stated in the paper that not having to re-learn visual representations when transferring between cities is one of the outcomes, but the tests do not actually check for this. There are likely significant differences in the features that would be learned in NYC vs. Waterloo, for example, and this type of transfer has not been evaluated.<br />
<br />
4. The proposed navigation model could be limited by its reliance on pre-defined landmarks, which appears to be strategically placed evenly spreading across each city. This could limit the agent's deployability to new cities.<br />
<br />
==Reference==<br />
[1] Espeholt, Lasse, Soyer, Hubert, Munos, Remi, Simonyan, Karen, Mnih, Volodymir, Ward, Tom, Doron, Yotam, Firoiu, Vlad, Harley, Tim, Dunning, Iain, Legg, Shane, and Kavukcuoglu, Koray. Impala: Scalable distributed deep-rl with importance weighted actor-learner architec- tures. arXiv preprint arXiv:1802.01561, 2018.<br />
<br />
[2] Mnih, Volodymyr, Badia, Adria Puigdomenech, Mirza, Mehdi, Graves, Alex, Lillicrap, Timothy, Harley, Tim, Silver, David, and Kavukcuoglu, Koray. Asynchronous methods for deep reinforcement learning. In Interna- tional Conference on Machine Learning, pp. 1928–1937, 2016.</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:picture1.png&diff=42325File:picture1.png2018-12-08T01:09:19Z<p>Aghabuss: </p>
<hr />
<div></div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=DON%27T_DECAY_THE_LEARNING_RATE_,_INCREASE_THE_BATCH_SIZE&diff=42095DON'T DECAY THE LEARNING RATE , INCREASE THE BATCH SIZE2018-11-30T17:30:25Z<p>Aghabuss: /* INTUITION */</p>
<hr />
<div>Summary of the ICLR 2018 paper: '''Don't Decay the learning Rate, Increase the Batch Size ''' <br />
<br />
Link: [https://arxiv.org/pdf/1711.00489.pdf]<br />
<br />
Summarized by: Afify, Ahmed [ID: 20700841]<br />
<br />
==INTUITION==<br />
Nowadays, it is a common practice not to have a singular steady learning rate for the learning phase of neural network models. Instead, we use adaptive learning rates with the standard gradient descent method. The intuition behind this is that when we are far away from the minima, it is beneficial for us to take large steps towards the minima, as it would require a lesser number of steps to converge, but as we approach the minima, our step size should decrease, otherwise we may just keep oscillating around the minima. In practice, this is generally achieved by methods like SGD with momentum, Nesterov momentum, and Adam. However, the core claim of this paper is that the same effect can be achieved by increasing the batch size during the gradient descent process while keeping the learning rate constant throughout. In addition, the paper argues that such an approach also reduces the parameter updates required to reach the minima, thus leading to greater parallelism and shorter training times. The authors present conclusive experimental evidence to prove the empirical benefits of decaying learning rate can be achieved by increasing the batch size instead.<br />
<br />
== INTRODUCTION ==<br />
Stochastic gradient descent (SGD) is the most widely used optimization technique for training deep learning models. The reason for this is that the minima found using this process generalizes well (Zhang et al., 2016; Wilson et al., 2017), but the optimization process is slow and time consuming as each parameter update corresponds to a small step towards the gooal. According to (Goyal et al., 2017; Hoffer et al., 2017; You et al., 2017a), this has motivated researchers to try to speed up this optimization process by taking bigger steps, and hence reduce the number of parameter updates in training a model. This can be achieved by using large batch training, which can be divided across many machines. <br />
<br />
However, increasing the batch size leads to decreasing the test set accuracy (Keskar et al., 2016; Goyal et al., 2017). Smith and Le (2017) believed that SGD has a scale of random fluctuations <math> g = \epsilon (\frac{N}{B}-1) </math>, where <math> \epsilon </math> is the learning rate, N number of training samples, and B batch size. They concluded that there is an optimal batch size proportional to the learning rate when <math> B \ll N </math>, and optimum fluctuation scale <math>g</math> at constant learning rate which maximizes test set accuracy. This was observed empirically by Goyal et al., 2017 and used to train a ResNet-50 in under an hour with 76.3% validation accuracy on ImageNet dataset.<br />
<br />
In this paper, the authors' main goal is to provide evidence that increasing the batch size is quantitatively equivalent to decreasing the learning rate. They show that this approach achieves almost equivalent model performance on the test set with the same number of training epochs but with remarkably fewer number of parameter updates. The strategy of increasing the batch size during training is in effect decreasing the scale of random fluctuations. Moreover, an additional reduction in the number of parameter updates can be attained by increasing the learning rate and scaling <math> B \propto \epsilon </math> or even more reduction by increasing the momentum coefficient and scaling <math> B \propto \frac{1}{1-m} </math> although the latter decreases the test accuracy. This has been demonstrated by several experiments on the ImageNet and CIFAR-10 datasets using ResNet-50 and Inception-ResNet-V2 architectures respectively.<br />
<br />
== STOCHASTIC GRADIENT DESCENT AND CONVEX OPTIMIZATION ==<br />
As mentioned in the previous section, the drawback of SGD when compared to full-batch training is the noise that it introduces that hinders optimization. According to (Robbins & Monro, 1951), there are two equations that govern how to reach the minimum of a convex function: (<math> \epsilon_i </math> denotes the learning rate at the <math> i^{th} </math> gradient update)<br />
<br />
<math> \sum_{i=1}^{\infty} \epsilon_i = \infty </math>. This equation guarantees that we will reach the minimum. <br />
<br />
<math> \sum_{i=1}^{\infty} \epsilon^2_i < \infty </math>. This equation, which is valid only for a fixed batch size, guarantees that learning rate decays fast enough allowing us to reach the minimum rather than bouncing due to noise.<br />
<br />
These equations indicate that the learning rate must decay during training, and second equation is only available when the batch size is constant. To change the batch size, Smith and Le (2017) proposed to interpret SGD as integrating this stochastic differential equation <math> \frac{dw}{dt} = -\frac{dC}{dw} + \eta(t) </math>, where <math>C</math> represents cost function, <math>w</math> represents the parameters, and <math>\eta</math> represents the Gaussian random noise. Furthermore, they proved that noise scale <math>g</math> controls the magnitude of random fluctuations in the training dynamics by this formula: <math> g = \epsilon (\frac{N}{B}-1) </math>, where <math> \epsilon </math> is the learning rate, N is the training set size and <math>B</math> is the batch size. As we usually have <math> B \ll N </math>, we can define <math> g \approx \epsilon \frac{N}{B} </math>. This explains why when the learning rate decreases, noise <math>g</math> decreases, enabling us to converge to the minimum of the cost function. However, increasing the batch size has the same effect and makes <math>g</math> decays with constant learning rate. In this work, the batch size is increased until <math> B \approx \frac{N}{10} </math>, then the conventional way of decaying the learning rate is followed.<br />
<br />
== SIMULATED ANNEALING AND THE GENERALIZATION GAP ==<br />
'''Simulated Annealing:''' decaying learning rates are empirically successful. To understand this, they note that introducing random fluctuations<br />
whose scale falls during training is also a well-established technique in non-convex optimization; simulated annealing. The initial noisy optimization phase allows exploring a larger fraction of the parameter space without becoming trapped in local minima. Once a promising region of parameter space is located, the noise is reduced to fine-tune the parameters.<br />
<br />
For more info: Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to approximate global optimization in a large search space for an optimization problem. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For problems where finding an approximate global optimum is more important than finding a precise local optimum in a fixed amount of time, simulated annealing may be preferable to alternatives such as gradient descent. [https://en.wikipedia.org/wiki/Simulated_annealing [Reference]]<br />
<br />
'''Generalization Gap:''' Small batch data generalizes better to the test set than large batch data.<br />
<br />
Smith and Le (2017) found that there is an optimal batch size which corresponds to optimal noise scale g <math> (g \approx \epsilon \frac{N}{B}) </math> and concluded that <math> B_{opt} \propto \epsilon N </math> that corresponds to maximum test set accuracy. This means that gradient noise is helpful as it makes SGD escape sharp minima, which does not generalize well. <br />
<br />
Simulated Annealing is a famous technique in non-convex optimization. Starting with noise in the training process helps us to discover a wide range of parameters then once we are near the optimum value, noise is reduced to fine tune our final parameters. However, more and more researches like to use the sharper decay schedules like cosine decay or step-function drops. In physical sciences, slowly annealing (or decaying) the temperature (which is the noise scale in this situation) helps to converge to the global minimum, which is sharp. But decaying the temperature in discrete steps can make the system stuck in a local minimum, which leads to higher cost and lower curvature. The authors think that deep learning has the same intuition.<br />
.<br />
<br />
== THE EFFECTIVE LEARNING RATE AND THE ACCUMULATION VARIABLE ==<br />
'''The Effective Learning Rate''' : <math> \epsilon_{eff} = \frac{\epsilon}{1-m} </math><br />
<br />
Smith and Le (2017) included momentum to the equation of the vanilla SGD noise scale that was defined above to be: <math> g = \frac{\epsilon}{1-m}(\frac{N}{B}-1)\approx \frac{\epsilon N}{B(1-m)} </math>, which is the same as the previous equation when m goes to 0. They found that increasing the learning rate and momentum coefficient and scaling <math> B \propto \frac{\epsilon }{1-m} </math> reduces the number of parameter updates, but the test accuracy decreases when the momentum coefficient is increased. <br />
<br />
To understand the reasons behind this, we need to analyze momentum update equations below:<br />
<br />
<center><math><br />
\Delta A = -(1-m)A + \frac{d\widehat{C}}{dw} <br />
</math><br />
<br />
<math><br />
\Delta w = -A\epsilon<br />
</math><br />
</center><br />
<br />
We can see that the Accumulation variable A, which is initially set to 0, then increases exponentially to reach its steady state value during <math> \frac{B}{N(1-m)} </math> training epochs while <math> \Delta w </math> is suppressed that can reduce the rate of convergence. Moreover, at high momentum, we have three challenges:<br />
<br />
1- Additional epochs are needed to catch up with the accumulation.<br />
<br />
2- Accumulation needs more time <math> \frac{B}{N(1-m)} </math> to forget old gradients. <br />
<br />
3- After this time, however, the accumulation cannot adapt to changes in the loss landscape.<br />
<br />
4- In the early stage, a large batch size will lead to the instabilities.<br />
<br />
== EXPERIMENTS ==<br />
=== SIMULATED ANNEALING IN A WIDE RESNET ===<br />
<br />
'''Dataset:''' CIFAR-10 (50,000 training images)<br />
<br />
'''Network Architecture:''' “16-4” wide ResNet<br />
<br />
'''Training Schedules used as in the below figure:''' . These demonstrate the equivalence between decreasing the learning rate and increasing the batch size.<br />
<br />
- Decaying learning rate: learning rate decays by a factor of 5 at a sequence of “steps”, and the batch size is constant<br />
<br />
- Increasing batch size: learning rate is constant, and the batch size is increased by a factor of 5 at every step.<br />
<br />
- Hybrid: At the beginning, the learning rate is constant and batch size is increased by a factor of 5. Then, the learning rate decays by a factor of 5 at each subsequent step, and the batch size is constant. This is the schedule that will be used if there is a hardware limit affecting a maximum batch size limit.<br />
<br />
If the learning rate itself must decay during training, then these schedules should show different learning curves (as a function of the number of training epochs) and reach different final test set accuracies. Meanwhile, if it is the noise scale which should decay, all three schedules should be indistinguishable.<br />
[[File:Paper_40_Fig_1.png | 800px|center]]<br />
<br />
As shown in the below figure: in the left figure (2a), we can observe that for the training set, the three learning curves are exactly the same while in figure 2b, increasing the batch size has a huge advantage of reducing the number of parameter updates.<br />
This concludes that noise scale is the one that needs to be decayed and not the learning rate itself<br />
[[File:Paper_40_Fig_2.png | 800px|center]] <br />
<br />
To make sure that these results are the same for the test set as well, in figure 3, we can see that the three learning curves are exactly the same for SGD with momentum, and Nesterov momentum<br />
[[File:Paper_40_Fig_3.png | 800px|center]]<br />
<br />
To check for other optimizers as well. the below figure shows the same experiment as in figure 3, which is the three learning curves for the test set, but for vanilla SGD and Adam, and showing <br />
[[File:Paper_40_Fig_4.png | 800px|center]]<br />
<br />
'''Conclusion:''' Decreasing the learning rate and increasing the batch size during training are equivalent<br />
<br />
=== INCREASING THE EFFECTIVE LEARNING RATE===<br />
<br />
Here, the focus is on minimizing the number of parameter updates required to train a model. As shown above, the first step is to replace decaying learning rates by increasing batch sizes. Now, the authors show here that we can also increase the effective learning rate <math>\epsilon_{eff} = \epsilon/(1 − m) </math> at the start of training, while scaling the initial batch size <math>B \propto \epsilon_{eff} </math> . All experiments are conducted using SGD with momentum. There are 50000 images in the CIFAR-10 training set, and since the scaling rules only hold when <math>B << N </math> , we decided to set a maximum batch size <math>B_{max} </math>= 5120 .<br />
<br />
'''Dataset:''' CIFAR-10 (50,000 training images)<br />
<br />
'''Network Architecture:''' “16-4” wide ResNet<br />
<br />
'''Training Parameters:''' Optimization Algorithm: SGD with momentum / Maximum batch size = 5120<br />
<br />
'''Training Schedules:''' <br />
<br />
The authors consider four training schedules, all of which decay the noise scale by a factor of five in a series of three steps with the same number of epochs.<br />
<br />
Original training schedule: initial learning rate of 0.1 which decays by a factor of 5 at each step, a momentum coefficient of 0.9, and a batch size of 128. Follows the implementation of Zagoruyko & Komodakis (2016).<br />
<br />
Increasing batch size: learning rate of 0.1, momentum coefficient of 0.9, initial batch size of 128 that increases by a factor of 5 at each step. <br />
<br />
Increased initial learning rate: initial learning rate of 0.5, initial batch size of 640 that increase during training.<br />
<br />
Increased momentum coefficient: increased initial learning rate of 0.5, initial batch size of 3200 that increase during training, and an increased momentum coefficient of 0.98.<br />
<br />
The results of all training schedules, which are presented in the below figure, are documented in the following table:<br />
<br />
[[File:Paper_40_Table_1.png | 800px|center]]<br />
<br />
[[File:Paper_40_Fig_5.png | 800px|center]]<br />
<br />
<br />
<br />
'''Conclusion:''' Increasing the effective learning rate and scaling the batch size results in further reduction in the number of parameter updates<br />
<br />
=== TRAINING IMAGENET IN 2500 PARAMETER UPDATES===<br />
<br />
'''A) Experiment Goal:''' Control Batch Size<br />
<br />
'''Dataset:''' ImageNet (1.28 million training images)<br />
<br />
The paper modified the setup of Goyal et al. (2017), and used the following configuration:<br />
<br />
'''Network Architecture:''' Inception-ResNet-V2 <br />
<br />
'''Training Parameters:''' <br />
<br />
90 epochs / noise decayed at epoch 30, 60, and 80 by a factor of 10 / Initial ghost batch size = 32 / Learning rate = 3 / momentum coefficient = 0.9 / Initial batch size = 8192<br />
<br />
Two training schedules were used:<br />
<br />
“Decaying learning rate”, where batch size is fixed and the learning rate is decayed<br />
<br />
“Increasing batch size”, where batch size is increased to 81920 then the learning rate is decayed at two steps.<br />
<br />
[[File:Paper_40_Table_2.png | 800px|center]]<br />
<br />
[[File:Paper_40_Fig_6.png | 800px|center]]<br />
<br />
'''Conclusion:''' Increasing the batch size resulted in reducing the number of parameter updates from 14,000 to 6,000.<br />
<br />
'''B) Experiment Goal:''' Control Batch Size and Momentum Coefficient<br />
<br />
'''Training Parameters:''' Ghost batch size = 64 / noise decayed at epoch 30, 60, and 80 by a factor of 10. <br />
<br />
The below table shows the number of parameter updates and accuracy for different sets of training parameters:<br />
<br />
[[File:Paper_40_Table_3.png | 800px|center]]<br />
<br />
[[File:Paper_40_Fig_7.png | 800px|center]]<br />
<br />
'''Conclusion:''' Increasing the momentum reduces the number of parameter updates, but leads to a drop in the test accuracy.<br />
<br />
=== TRAINING IMAGENET IN 30 MINUTES===<br />
<br />
'''Dataset:''' ImageNet (Already introduced in the previous section)<br />
<br />
'''Network Architecture:''' ResNet-50<br />
<br />
The paper replicated the setup of Goyal et al. (2017) while modifying the number of TPU devices, batch size, learning rate, and then calculating the time to complete 90 epochs, and measuring the accuracy, and performed the following experiments below:<br />
<br />
[[File:Paper_40_Table_4.png | 800px|center]]<br />
<br />
'''Conclusion:''' Model training times can be reduced by increasing the batch size during training.<br />
<br />
== RELATED WORK ==<br />
Main related work mentioned in the paper is as follows:<br />
<br />
- Smith & Le (2017) interpreted Stochastic gradient descent as stochastic differential equation; the paper built on this idea to include decaying learning rate.<br />
<br />
- Mandt et al. (2017) analyzed how to modify SGD for the task of Bayesian posterior sampling.<br />
<br />
- Keskar et al. (2016) focused on the analysis of noise once the training is started.<br />
<br />
- Moreover, the proportional relationship between batch size and learning rate was first discovered by Goyal et al. (2017) and successfully trained ResNet-50 on ImageNet in one hour after discovering the proportionality relationship between batch size and learning rate.<br />
<br />
- Furthermore, You et al. (2017a) presented Layer-wise Adaptive Rate Scaling (LARS), which is applying different learning rates to train ImageNet in 14 minutes and 74.9% accuracy. <br />
<br />
- Wilson et al. (2017) argued that adaptive optimization methods tend to generalize less well than SGD and SGD with momentum (although<br />
they did not include K-FAC in their study), while the authors' work reduces the gap in convergence speed.<br />
<br />
- Finally, another strategy called Asynchronous-SGD that allowed (Recht et al., 2011; Dean et al., 2012) to use multiple GPUs even with small batch sizes.<br />
<br />
== CONCLUSIONS ==<br />
Increasing the batch size during training has the same benefits of decaying the learning rate in addition to reducing the number of parameter updates, which corresponds to faster training time. Experiments were performed on different image datasets and various optimizers with different training schedules to prove this result. The paper proposed to increase the learning rate and momentum parameter <math>m</math>, while scaling <math> B \propto \frac{\epsilon}{1-m} </math>, which achieves fewer parameter updates, but slightly less test set accuracy as mentioned in detail in the experiments’ section. In summary, on ImageNet dataset, Inception-ResNet-V2 achieved 77% validation accuracy in under 2500 parameter updates, and ResNet-50 achieved 76.1% validation set accuracy on TPU in less than 30 minutes. One of the great findings of this paper is that all the methods use the hyper-parameters directly from previous works in the literature, and no additional hyper-parameter tuning was performed.<br />
<br />
== CRITIQUE ==<br />
'''Pros:'''<br />
<br />
- The paper showed empirically that increasing batch size and decaying learning rate are equivalent.<br />
<br />
- Several experiments were performed on different optimizers such as SGD and Adam.<br />
<br />
- Had several comparisons with previous experimental setups.<br />
<br />
'''Cons:'''<br />
<br />
<br />
- All datasets used are image datasets. Other experiments should have been done on datasets from different domains to ensure generalization. <br />
<br />
- The number of parameter updates was used as a comparison criterion, but wall-clock times could have provided additional measurable judgment although they depend on the hardware used.<br />
<br />
- Special hardware is needed for large batch training, which is not always feasible. As batch-size increases, we generally need more RAM to train the same model. However, if learning rate is decreased, the RAM use remains constant. As a result, learning rate decay will allow us to train bigger models.<br />
<br />
- In section 5.2 (Increasing the Effective Learning rate), the authors did not test a range of learning rate values and used only (0.1 and 0.5). Additional results from varying the initial learning rate values from 0.1 to 3.2 are provided in the appendix, which indicates that the test accuracy begins to fall for initial learning rates greater than ~0.4. The appended results do not show validation set accuracy curves like in Figure 6, however. It would be beneficial to see if they were similar to the original 0.1 and 0.5 initial learning rate baselines.<br />
<br />
- Although the main idea of the paper is interesting, its results do not seem to be too surprising in comparison with other recent papers in the subject.<br />
<br />
- The paper could benefit from using some other models to demonstrate its claim and generalize its idea by adding some comparisons with other models as well as other recent methods to increase batch size.<br />
<br />
- The paper presents interesting ideas. However, it lacks mathematical and theoretical analysis beyond the idea. Since the experiment is primary on image dataset and it does not provide sufficient theories, the paper itself presents limited applicability to other types. <br />
<br />
- Also, in experimental setting, only single training runs from one random initialization is used. It would be better to take the best of many runs or to show confidence intervals.<br />
<br />
- It is proposed that we should compare learning rate decay with batch-size increase under the setting that total budget / number of training samples is fixed.<br />
<br />
== REFERENCES ==<br />
# Takuya Akiba, Shuji Suzuki, and Keisuke Fukuda. Extremely large minibatch sgd: Training resnet-50 on imagenet in 15 minutes. arXiv preprint arXiv:1711.04325, 2017.<br />
#Lukas Balles, Javier Romero, and Philipp Hennig. Coupling adaptive batch sizes with learning rates.arXiv preprint arXiv:1612.05086, 2016.<br />
#L´eon Bottou, Frank E Curtis, and Jorge Nocedal. Optimization methods for large-scale machine learning.arXiv preprint arXiv:1606.04838, 2016.<br />
#Richard H Byrd, Gillian M Chin, Jorge Nocedal, and Yuchen Wu. Sample size selection in optimization methods for machine learning. Mathematical programming, 134(1):127–155, 2012.<br />
#Pratik Chaudhari, Anna Choromanska, Stefano Soatto, and Yann LeCun. Entropy-SGD: Biasing gradient descent into wide valleys. arXiv preprint arXiv:1611.01838, 2016.<br />
#Soham De, Abhay Yadav, David Jacobs, and Tom Goldstein. Automated inference with adaptive batches. In Artificial Intelligence and Statistics, pp. 1504–1513, 2017.<br />
#Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Andrew Senior, Paul Tucker, Ke Yang, Quoc V Le, et al. Large scale distributed deep networks. In Advances in neural information processing systems, pp. 1223–1231, 2012.<br />
#Michael P Friedlander and Mark Schmidt. Hybrid deterministic-stochastic methods for data fitting.SIAM Journal on Scientific Computing, 34(3):A1380–A1405, 2012.<br />
#Priya Goyal, Piotr Doll´ar, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He. Accurate, large minibatch SGD: Training imagenet in 1 hour. arXiv preprint arXiv:1706.02677, 2017.<br />
#Sepp Hochreiter and J¨urgen Schmidhuber. Flat minima. Neural Computation, 9(1):1–42, 1997.<br />
#Elad Hoffer, Itay Hubara, and Daniel Soudry. Train longer, generalize better: closing the generalization gap in large batch training of neural networks. arXiv preprint arXiv:1705.08741, 2017.<br />
#Norman P Jouppi, Cliff Young, Nishant Patil, David Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates, Suresh Bhatia, Nan Boden, Al Borchers, et al. In-datacenter performance analysis of a tensor processing unit. In Proceedings of the 44th Annual International Symposium on Computer Architecture, pp. 1–12. ACM, 2017.<br />
#Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang. On large-batch training for deep learning: Generalization gap and sharp minima. arXiv preprint arXiv:1609.04836, 2016.<br />
#Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.<br />
#Alex Krizhevsky. One weird trick for parallelizing convolutional neural networks. arXiv preprint arXiv:1404.5997, 2014.<br />
#Qianxiao Li, Cheng Tai, and E Weinan. Stochastic modified equations and adaptive stochastic gradient algorithms. arXiv preprint arXiv:1511.06251, 2017.<br />
#Ilya Loshchilov and Frank Hutter. SGDR: stochastic gradient descent with restarts. arXiv preprint arXiv:1608.03983, 2016.<br />
#Stephan Mandt, Matthew D Hoffman, and DavidMBlei. Stochastic gradient descent as approximate bayesian inference. arXiv preprint arXiv:1704.04289, 2017.<br />
#James Martens and Roger Grosse. Optimizing neural networks with kronecker-factored approximate curvature. In International Conference on Machine Learning, pp. 2408–2417, 2015.<br />
#Yurii Nesterov. A method of solving a convex programming problem with convergence rate o (1/k2). In Soviet Mathematics Doklady, volume 27, pp. 372–376, 1983.<br />
#Lutz Prechelt. Early stopping-but when? Neural Networks: Tricks of the trade, pp. 553–553, 1998.<br />
#Benjamin Recht, Christopher Re, Stephen Wright, and Feng Niu. Hogwild: A lock-free approach to parallelizing stochastic gradient descent. In Advances in neural information processing systems, pp. 693–701, 2011.<br />
#Herbert Robbins and Sutton Monro. A stochastic approximation method. The annals of mathematical statistics, pp. 400–407, 1951.<br />
#Samuel L. Smith and Quoc V. Le. A bayesian perspective on generalization and stochastic gradient descent. arXiv preprint arXiv:1710.06451, 2017.<br />
#Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi. Inception-v4, Inception-ResNet and the impact of residual connections on learning. In AAAI, pp. 4278–4284, 2017.<br />
#Max Welling and Yee W Teh. Bayesian learning via stochastic gradient langevin dynamics. In Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 681–688, 2011.<br />
#Ashia C Wilson, Rebecca Roelofs, Mitchell Stern, Nathan Srebro, and Benjamin Recht. The marginal value of adaptive gradient methods in machine learning. arXiv preprint arXiv:1705.08292, 2017.<br />
#Yang You, Igor Gitman, and Boris Ginsburg. Scaling SGD batch size to 32k for imagenet training. arXiv preprint arXiv:1708.03888, 2017a.<br />
#Yang You, Zhao Zhang, C Hsieh, James Demmel, and Kurt Keutzer. Imagenet training in minutes. CoRR, abs/1709.05011, 2017b.<br />
#Sergey Zagoruyko and Nikos Komodakis. Wide residual networks. arXiv preprint arXiv:1605.07146, 2016.<br />
#Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. Understanding deep learning requires rethinking generalization. arXiv preprint arXiv:1611.03530, 2016.</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=DON%27T_DECAY_THE_LEARNING_RATE_,_INCREASE_THE_BATCH_SIZE&diff=42094DON'T DECAY THE LEARNING RATE , INCREASE THE BATCH SIZE2018-11-30T17:27:55Z<p>Aghabuss: </p>
<hr />
<div>Summary of the ICLR 2018 paper: '''Don't Decay the learning Rate, Increase the Batch Size ''' <br />
<br />
Link: [https://arxiv.org/pdf/1711.00489.pdf]<br />
<br />
Summarized by: Afify, Ahmed [ID: 20700841]<br />
<br />
==INTUITION==<br />
Nowadays, it is a common practice not to have a singular steady learning rate for the learning phase of neural network models. Instead, we use adaptive learning rates with the standard gradient descent method. The intuition behind this is that when we are far away from the minima, it is beneficial for us to take large steps towards the minima, as it would require a lesser number of steps to converge, but as we approach the minima, our step size should decrease, otherwise we may just keep oscillating around the minima. In practice, this is generally achieved by methods like SGD with momentum, Nesterov momentum, and Adam. However, the core claim of this paper is that the same effect can be achieved by increasing the batch size during the gradient descent process while keeping the learning rate constant throughout. In addition, the paper argues that such an approach also reduces the parameter updates required to reach the minima, thus leading to greater parallelism and shorter training times.<br />
<br />
== INTRODUCTION ==<br />
Stochastic gradient descent (SGD) is the most widely used optimization technique for training deep learning models. The reason for this is that the minima found using this process generalizes well (Zhang et al., 2016; Wilson et al., 2017), but the optimization process is slow and time consuming as each parameter update corresponds to a small step towards the gooal. According to (Goyal et al., 2017; Hoffer et al., 2017; You et al., 2017a), this has motivated researchers to try to speed up this optimization process by taking bigger steps, and hence reduce the number of parameter updates in training a model. This can be achieved by using large batch training, which can be divided across many machines. <br />
<br />
However, increasing the batch size leads to decreasing the test set accuracy (Keskar et al., 2016; Goyal et al., 2017). Smith and Le (2017) believed that SGD has a scale of random fluctuations <math> g = \epsilon (\frac{N}{B}-1) </math>, where <math> \epsilon </math> is the learning rate, N number of training samples, and B batch size. They concluded that there is an optimal batch size proportional to the learning rate when <math> B \ll N </math>, and optimum fluctuation scale <math>g</math> at constant learning rate which maximizes test set accuracy. This was observed empirically by Goyal et al., 2017 and used to train a ResNet-50 in under an hour with 76.3% validation accuracy on ImageNet dataset.<br />
<br />
In this paper, the authors' main goal is to provide evidence that increasing the batch size is quantitatively equivalent to decreasing the learning rate. They show that this approach achieves almost equivalent model performance on the test set with the same number of training epochs but with remarkably fewer number of parameter updates. The strategy of increasing the batch size during training is in effect decreasing the scale of random fluctuations. Moreover, an additional reduction in the number of parameter updates can be attained by increasing the learning rate and scaling <math> B \propto \epsilon </math> or even more reduction by increasing the momentum coefficient and scaling <math> B \propto \frac{1}{1-m} </math> although the latter decreases the test accuracy. This has been demonstrated by several experiments on the ImageNet and CIFAR-10 datasets using ResNet-50 and Inception-ResNet-V2 architectures respectively.<br />
<br />
== STOCHASTIC GRADIENT DESCENT AND CONVEX OPTIMIZATION ==<br />
As mentioned in the previous section, the drawback of SGD when compared to full-batch training is the noise that it introduces that hinders optimization. According to (Robbins & Monro, 1951), there are two equations that govern how to reach the minimum of a convex function: (<math> \epsilon_i </math> denotes the learning rate at the <math> i^{th} </math> gradient update)<br />
<br />
<math> \sum_{i=1}^{\infty} \epsilon_i = \infty </math>. This equation guarantees that we will reach the minimum. <br />
<br />
<math> \sum_{i=1}^{\infty} \epsilon^2_i < \infty </math>. This equation, which is valid only for a fixed batch size, guarantees that learning rate decays fast enough allowing us to reach the minimum rather than bouncing due to noise.<br />
<br />
These equations indicate that the learning rate must decay during training, and second equation is only available when the batch size is constant. To change the batch size, Smith and Le (2017) proposed to interpret SGD as integrating this stochastic differential equation <math> \frac{dw}{dt} = -\frac{dC}{dw} + \eta(t) </math>, where <math>C</math> represents cost function, <math>w</math> represents the parameters, and <math>\eta</math> represents the Gaussian random noise. Furthermore, they proved that noise scale <math>g</math> controls the magnitude of random fluctuations in the training dynamics by this formula: <math> g = \epsilon (\frac{N}{B}-1) </math>, where <math> \epsilon </math> is the learning rate, N is the training set size and <math>B</math> is the batch size. As we usually have <math> B \ll N </math>, we can define <math> g \approx \epsilon \frac{N}{B} </math>. This explains why when the learning rate decreases, noise <math>g</math> decreases, enabling us to converge to the minimum of the cost function. However, increasing the batch size has the same effect and makes <math>g</math> decays with constant learning rate. In this work, the batch size is increased until <math> B \approx \frac{N}{10} </math>, then the conventional way of decaying the learning rate is followed.<br />
<br />
== SIMULATED ANNEALING AND THE GENERALIZATION GAP ==<br />
'''Simulated Annealing:''' decaying learning rates are empirically successful. To understand this, they note that introducing random fluctuations<br />
whose scale falls during training is also a well-established technique in non-convex optimization; simulated annealing. The initial noisy optimization phase allows exploring a larger fraction of the parameter space without becoming trapped in local minima. Once a promising region of parameter space is located, the noise is reduced to fine-tune the parameters.<br />
<br />
For more info: Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to approximate global optimization in a large search space for an optimization problem. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For problems where finding an approximate global optimum is more important than finding a precise local optimum in a fixed amount of time, simulated annealing may be preferable to alternatives such as gradient descent. [https://en.wikipedia.org/wiki/Simulated_annealing [Reference]]<br />
<br />
'''Generalization Gap:''' Small batch data generalizes better to the test set than large batch data.<br />
<br />
Smith and Le (2017) found that there is an optimal batch size which corresponds to optimal noise scale g <math> (g \approx \epsilon \frac{N}{B}) </math> and concluded that <math> B_{opt} \propto \epsilon N </math> that corresponds to maximum test set accuracy. This means that gradient noise is helpful as it makes SGD escape sharp minima, which does not generalize well. <br />
<br />
Simulated Annealing is a famous technique in non-convex optimization. Starting with noise in the training process helps us to discover a wide range of parameters then once we are near the optimum value, noise is reduced to fine tune our final parameters. However, more and more researches like to use the sharper decay schedules like cosine decay or step-function drops. In physical sciences, slowly annealing (or decaying) the temperature (which is the noise scale in this situation) helps to converge to the global minimum, which is sharp. But decaying the temperature in discrete steps can make the system stuck in a local minimum, which leads to higher cost and lower curvature. The authors think that deep learning has the same intuition.<br />
.<br />
<br />
== THE EFFECTIVE LEARNING RATE AND THE ACCUMULATION VARIABLE ==<br />
'''The Effective Learning Rate''' : <math> \epsilon_{eff} = \frac{\epsilon}{1-m} </math><br />
<br />
Smith and Le (2017) included momentum to the equation of the vanilla SGD noise scale that was defined above to be: <math> g = \frac{\epsilon}{1-m}(\frac{N}{B}-1)\approx \frac{\epsilon N}{B(1-m)} </math>, which is the same as the previous equation when m goes to 0. They found that increasing the learning rate and momentum coefficient and scaling <math> B \propto \frac{\epsilon }{1-m} </math> reduces the number of parameter updates, but the test accuracy decreases when the momentum coefficient is increased. <br />
<br />
To understand the reasons behind this, we need to analyze momentum update equations below:<br />
<br />
<center><math><br />
\Delta A = -(1-m)A + \frac{d\widehat{C}}{dw} <br />
</math><br />
<br />
<math><br />
\Delta w = -A\epsilon<br />
</math><br />
</center><br />
<br />
We can see that the Accumulation variable A, which is initially set to 0, then increases exponentially to reach its steady state value during <math> \frac{B}{N(1-m)} </math> training epochs while <math> \Delta w </math> is suppressed that can reduce the rate of convergence. Moreover, at high momentum, we have three challenges:<br />
<br />
1- Additional epochs are needed to catch up with the accumulation.<br />
<br />
2- Accumulation needs more time <math> \frac{B}{N(1-m)} </math> to forget old gradients. <br />
<br />
3- After this time, however, the accumulation cannot adapt to changes in the loss landscape.<br />
<br />
4- In the early stage, a large batch size will lead to the instabilities.<br />
<br />
== EXPERIMENTS ==<br />
=== SIMULATED ANNEALING IN A WIDE RESNET ===<br />
<br />
'''Dataset:''' CIFAR-10 (50,000 training images)<br />
<br />
'''Network Architecture:''' “16-4” wide ResNet<br />
<br />
'''Training Schedules used as in the below figure:''' . These demonstrate the equivalence between decreasing the learning rate and increasing the batch size.<br />
<br />
- Decaying learning rate: learning rate decays by a factor of 5 at a sequence of “steps”, and the batch size is constant<br />
<br />
- Increasing batch size: learning rate is constant, and the batch size is increased by a factor of 5 at every step.<br />
<br />
- Hybrid: At the beginning, the learning rate is constant and batch size is increased by a factor of 5. Then, the learning rate decays by a factor of 5 at each subsequent step, and the batch size is constant. This is the schedule that will be used if there is a hardware limit affecting a maximum batch size limit.<br />
<br />
If the learning rate itself must decay during training, then these schedules should show different learning curves (as a function of the number of training epochs) and reach different final test set accuracies. Meanwhile, if it is the noise scale which should decay, all three schedules should be indistinguishable.<br />
[[File:Paper_40_Fig_1.png | 800px|center]]<br />
<br />
As shown in the below figure: in the left figure (2a), we can observe that for the training set, the three learning curves are exactly the same while in figure 2b, increasing the batch size has a huge advantage of reducing the number of parameter updates.<br />
This concludes that noise scale is the one that needs to be decayed and not the learning rate itself<br />
[[File:Paper_40_Fig_2.png | 800px|center]] <br />
<br />
To make sure that these results are the same for the test set as well, in figure 3, we can see that the three learning curves are exactly the same for SGD with momentum, and Nesterov momentum<br />
[[File:Paper_40_Fig_3.png | 800px|center]]<br />
<br />
To check for other optimizers as well. the below figure shows the same experiment as in figure 3, which is the three learning curves for the test set, but for vanilla SGD and Adam, and showing <br />
[[File:Paper_40_Fig_4.png | 800px|center]]<br />
<br />
'''Conclusion:''' Decreasing the learning rate and increasing the batch size during training are equivalent<br />
<br />
=== INCREASING THE EFFECTIVE LEARNING RATE===<br />
<br />
Here, the focus is on minimizing the number of parameter updates required to train a model. As shown above, the first step is to replace decaying learning rates by increasing batch sizes. Now, the authors show here that we can also increase the effective learning rate <math>\epsilon_{eff} = \epsilon/(1 − m) </math> at the start of training, while scaling the initial batch size <math>B \propto \epsilon_{eff} </math> . All experiments are conducted using SGD with momentum. There are 50000 images in the CIFAR-10 training set, and since the scaling rules only hold when <math>B << N </math> , we decided to set a maximum batch size <math>B_{max} </math>= 5120 .<br />
<br />
'''Dataset:''' CIFAR-10 (50,000 training images)<br />
<br />
'''Network Architecture:''' “16-4” wide ResNet<br />
<br />
'''Training Parameters:''' Optimization Algorithm: SGD with momentum / Maximum batch size = 5120<br />
<br />
'''Training Schedules:''' <br />
<br />
The authors consider four training schedules, all of which decay the noise scale by a factor of five in a series of three steps with the same number of epochs.<br />
<br />
Original training schedule: initial learning rate of 0.1 which decays by a factor of 5 at each step, a momentum coefficient of 0.9, and a batch size of 128. Follows the implementation of Zagoruyko & Komodakis (2016).<br />
<br />
Increasing batch size: learning rate of 0.1, momentum coefficient of 0.9, initial batch size of 128 that increases by a factor of 5 at each step. <br />
<br />
Increased initial learning rate: initial learning rate of 0.5, initial batch size of 640 that increase during training.<br />
<br />
Increased momentum coefficient: increased initial learning rate of 0.5, initial batch size of 3200 that increase during training, and an increased momentum coefficient of 0.98.<br />
<br />
The results of all training schedules, which are presented in the below figure, are documented in the following table:<br />
<br />
[[File:Paper_40_Table_1.png | 800px|center]]<br />
<br />
[[File:Paper_40_Fig_5.png | 800px|center]]<br />
<br />
<br />
<br />
'''Conclusion:''' Increasing the effective learning rate and scaling the batch size results in further reduction in the number of parameter updates<br />
<br />
=== TRAINING IMAGENET IN 2500 PARAMETER UPDATES===<br />
<br />
'''A) Experiment Goal:''' Control Batch Size<br />
<br />
'''Dataset:''' ImageNet (1.28 million training images)<br />
<br />
The paper modified the setup of Goyal et al. (2017), and used the following configuration:<br />
<br />
'''Network Architecture:''' Inception-ResNet-V2 <br />
<br />
'''Training Parameters:''' <br />
<br />
90 epochs / noise decayed at epoch 30, 60, and 80 by a factor of 10 / Initial ghost batch size = 32 / Learning rate = 3 / momentum coefficient = 0.9 / Initial batch size = 8192<br />
<br />
Two training schedules were used:<br />
<br />
“Decaying learning rate”, where batch size is fixed and the learning rate is decayed<br />
<br />
“Increasing batch size”, where batch size is increased to 81920 then the learning rate is decayed at two steps.<br />
<br />
[[File:Paper_40_Table_2.png | 800px|center]]<br />
<br />
[[File:Paper_40_Fig_6.png | 800px|center]]<br />
<br />
'''Conclusion:''' Increasing the batch size resulted in reducing the number of parameter updates from 14,000 to 6,000.<br />
<br />
'''B) Experiment Goal:''' Control Batch Size and Momentum Coefficient<br />
<br />
'''Training Parameters:''' Ghost batch size = 64 / noise decayed at epoch 30, 60, and 80 by a factor of 10. <br />
<br />
The below table shows the number of parameter updates and accuracy for different sets of training parameters:<br />
<br />
[[File:Paper_40_Table_3.png | 800px|center]]<br />
<br />
[[File:Paper_40_Fig_7.png | 800px|center]]<br />
<br />
'''Conclusion:''' Increasing the momentum reduces the number of parameter updates, but leads to a drop in the test accuracy.<br />
<br />
=== TRAINING IMAGENET IN 30 MINUTES===<br />
<br />
'''Dataset:''' ImageNet (Already introduced in the previous section)<br />
<br />
'''Network Architecture:''' ResNet-50<br />
<br />
The paper replicated the setup of Goyal et al. (2017) while modifying the number of TPU devices, batch size, learning rate, and then calculating the time to complete 90 epochs, and measuring the accuracy, and performed the following experiments below:<br />
<br />
[[File:Paper_40_Table_4.png | 800px|center]]<br />
<br />
'''Conclusion:''' Model training times can be reduced by increasing the batch size during training.<br />
<br />
== RELATED WORK ==<br />
Main related work mentioned in the paper is as follows:<br />
<br />
- Smith & Le (2017) interpreted Stochastic gradient descent as stochastic differential equation; the paper built on this idea to include decaying learning rate.<br />
<br />
- Mandt et al. (2017) analyzed how to modify SGD for the task of Bayesian posterior sampling.<br />
<br />
- Keskar et al. (2016) focused on the analysis of noise once the training is started.<br />
<br />
- Moreover, the proportional relationship between batch size and learning rate was first discovered by Goyal et al. (2017) and successfully trained ResNet-50 on ImageNet in one hour after discovering the proportionality relationship between batch size and learning rate.<br />
<br />
- Furthermore, You et al. (2017a) presented Layer-wise Adaptive Rate Scaling (LARS), which is applying different learning rates to train ImageNet in 14 minutes and 74.9% accuracy. <br />
<br />
- Wilson et al. (2017) argued that adaptive optimization methods tend to generalize less well than SGD and SGD with momentum (although<br />
they did not include K-FAC in their study), while the authors' work reduces the gap in convergence speed.<br />
<br />
- Finally, another strategy called Asynchronous-SGD that allowed (Recht et al., 2011; Dean et al., 2012) to use multiple GPUs even with small batch sizes.<br />
<br />
== CONCLUSIONS ==<br />
Increasing the batch size during training has the same benefits of decaying the learning rate in addition to reducing the number of parameter updates, which corresponds to faster training time. Experiments were performed on different image datasets and various optimizers with different training schedules to prove this result. The paper proposed to increase the learning rate and momentum parameter <math>m</math>, while scaling <math> B \propto \frac{\epsilon}{1-m} </math>, which achieves fewer parameter updates, but slightly less test set accuracy as mentioned in detail in the experiments’ section. In summary, on ImageNet dataset, Inception-ResNet-V2 achieved 77% validation accuracy in under 2500 parameter updates, and ResNet-50 achieved 76.1% validation set accuracy on TPU in less than 30 minutes. One of the great findings of this paper is that all the methods use the hyper-parameters directly from previous works in the literature, and no additional hyper-parameter tuning was performed.<br />
<br />
== CRITIQUE ==<br />
'''Pros:'''<br />
<br />
- The paper showed empirically that increasing batch size and decaying learning rate are equivalent.<br />
<br />
- Several experiments were performed on different optimizers such as SGD and Adam.<br />
<br />
- Had several comparisons with previous experimental setups.<br />
<br />
'''Cons:'''<br />
<br />
<br />
- All datasets used are image datasets. Other experiments should have been done on datasets from different domains to ensure generalization. <br />
<br />
- The number of parameter updates was used as a comparison criterion, but wall-clock times could have provided additional measurable judgment although they depend on the hardware used.<br />
<br />
- Special hardware is needed for large batch training, which is not always feasible. As batch-size increases, we generally need more RAM to train the same model. However, if learning rate is decreased, the RAM use remains constant. As a result, learning rate decay will allow us to train bigger models.<br />
<br />
- In section 5.2 (Increasing the Effective Learning rate), the authors did not test a range of learning rate values and used only (0.1 and 0.5). Additional results from varying the initial learning rate values from 0.1 to 3.2 are provided in the appendix, which indicates that the test accuracy begins to fall for initial learning rates greater than ~0.4. The appended results do not show validation set accuracy curves like in Figure 6, however. It would be beneficial to see if they were similar to the original 0.1 and 0.5 initial learning rate baselines.<br />
<br />
- Although the main idea of the paper is interesting, its results do not seem to be too surprising in comparison with other recent papers in the subject.<br />
<br />
- The paper could benefit from using some other models to demonstrate its claim and generalize its idea by adding some comparisons with other models as well as other recent methods to increase batch size.<br />
<br />
- The paper presents interesting ideas. However, it lacks mathematical and theoretical analysis beyond the idea. Since the experiment is primary on image dataset and it does not provide sufficient theories, the paper itself presents limited applicability to other types. <br />
<br />
- Also, in experimental setting, only single training runs from one random initialization is used. It would be better to take the best of many runs or to show confidence intervals.<br />
<br />
- It is proposed that we should compare learning rate decay with batch-size increase under the setting that total budget / number of training samples is fixed.<br />
<br />
== REFERENCES ==<br />
# Takuya Akiba, Shuji Suzuki, and Keisuke Fukuda. Extremely large minibatch sgd: Training resnet-50 on imagenet in 15 minutes. arXiv preprint arXiv:1711.04325, 2017.<br />
#Lukas Balles, Javier Romero, and Philipp Hennig. Coupling adaptive batch sizes with learning rates.arXiv preprint arXiv:1612.05086, 2016.<br />
#L´eon Bottou, Frank E Curtis, and Jorge Nocedal. Optimization methods for large-scale machine learning.arXiv preprint arXiv:1606.04838, 2016.<br />
#Richard H Byrd, Gillian M Chin, Jorge Nocedal, and Yuchen Wu. Sample size selection in optimization methods for machine learning. Mathematical programming, 134(1):127–155, 2012.<br />
#Pratik Chaudhari, Anna Choromanska, Stefano Soatto, and Yann LeCun. Entropy-SGD: Biasing gradient descent into wide valleys. arXiv preprint arXiv:1611.01838, 2016.<br />
#Soham De, Abhay Yadav, David Jacobs, and Tom Goldstein. Automated inference with adaptive batches. In Artificial Intelligence and Statistics, pp. 1504–1513, 2017.<br />
#Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Andrew Senior, Paul Tucker, Ke Yang, Quoc V Le, et al. Large scale distributed deep networks. In Advances in neural information processing systems, pp. 1223–1231, 2012.<br />
#Michael P Friedlander and Mark Schmidt. Hybrid deterministic-stochastic methods for data fitting.SIAM Journal on Scientific Computing, 34(3):A1380–A1405, 2012.<br />
#Priya Goyal, Piotr Doll´ar, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He. Accurate, large minibatch SGD: Training imagenet in 1 hour. arXiv preprint arXiv:1706.02677, 2017.<br />
#Sepp Hochreiter and J¨urgen Schmidhuber. Flat minima. Neural Computation, 9(1):1–42, 1997.<br />
#Elad Hoffer, Itay Hubara, and Daniel Soudry. Train longer, generalize better: closing the generalization gap in large batch training of neural networks. arXiv preprint arXiv:1705.08741, 2017.<br />
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#Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang. On large-batch training for deep learning: Generalization gap and sharp minima. arXiv preprint arXiv:1609.04836, 2016.<br />
#Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.<br />
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#Stephan Mandt, Matthew D Hoffman, and DavidMBlei. Stochastic gradient descent as approximate bayesian inference. arXiv preprint arXiv:1704.04289, 2017.<br />
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#Benjamin Recht, Christopher Re, Stephen Wright, and Feng Niu. Hogwild: A lock-free approach to parallelizing stochastic gradient descent. In Advances in neural information processing systems, pp. 693–701, 2011.<br />
#Herbert Robbins and Sutton Monro. A stochastic approximation method. The annals of mathematical statistics, pp. 400–407, 1951.<br />
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#Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi. Inception-v4, Inception-ResNet and the impact of residual connections on learning. In AAAI, pp. 4278–4284, 2017.<br />
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#Yang You, Igor Gitman, and Boris Ginsburg. Scaling SGD batch size to 32k for imagenet training. arXiv preprint arXiv:1708.03888, 2017a.<br />
#Yang You, Zhao Zhang, C Hsieh, James Demmel, and Kurt Keutzer. Imagenet training in minutes. CoRR, abs/1709.05011, 2017b.<br />
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#Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. Understanding deep learning requires rethinking generalization. arXiv preprint arXiv:1611.03530, 2016.</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=DETECTING_STATISTICAL_INTERACTIONS_FROM_NEURAL_NETWORK_WEIGHTS&diff=42091DETECTING STATISTICAL INTERACTIONS FROM NEURAL NETWORK WEIGHTS2018-11-30T17:19:24Z<p>Aghabuss: </p>
<hr />
<div>=Introduction=<br />
<br />
It has been commonly believed that one major advantage of neural networks is their capability of modelling complex statistical interactions between features for automatic feature learning. Statistical interactions capture important information on where features often have joint effects with other features on predicting an outcome. The discovery of interactions is especially useful for scientific discoveries and hypothesis validation. For example, physicists may be interested in understanding what joint factors provide evidence for new elementary particles; doctors may want to know what interactions are accounted for in risk prediction models, to compare against known interactions from existing medical literature.<br />
<br />
With the growth in the computational power available Neural Networks have been able to solve many of the complex tasks in a wide variety of fields. This is mainly due to their ability to model complex and non-linear interactions. Neural networks have traditionally been treated as “black box” models, preventing their adoption in many application domains, such as those where explainability is desirable. It has been noted that complex machine learning models can learn unintended patterns from data, raising significant risks to stakeholders [14]. Therefore, in applications where machine learning models are intended for making critical decisions, such as healthcare or finance, it is paramount to understand how they make predictions [9]. Within several areas, like eg: computation social science, interpretability is of utmost importance. Since we do not understand how a neural network comes to its decision, practitioners in these areas tend to prefer simpler models like linear regression, decision trees, etc. which are much more interpretable. In this paper, we are going to present one way of implementing interpretability in a neural network.<br />
<br />
Existing approaches to interpreting neural networks can be summarized into two types. One type is direct interpretation, which focuses on 1) explaining individual feature importance, for example by computing input gradients [13] and decomposing predictions [8], 2) developing attention-based models, which illustrate where neural networks focus during inference [11], and 3) providing model-specific visualizations, such as feature map and gate activation visualizations [15]. The other type is indirect interpretation, for example post-hoc interpretations of feature importance [12] and knowledge distillation to simpler interpretable models [10].<br />
<br />
In this paper, the authors propose Neural Interaction Detection (NID), which can detect any order or form of statistical interaction captured by the feedforward neural network by examining its weight matrix.<br />
<br />
Note that in this paper, we only consider one specific types of neural network, feedforward neural network. Based on the methodology discussed here, the authors suggest that we can build an interpretation method for other types of networks also.<br />
<br />
=Related Work=<br />
<br />
1. Interaction Detection approaches: <br />
* Conduct individual tests for all features' combination such as ANOVA and Additive Groves.<br />
* Define all interaction forms of interest, then later finds the important ones.<br />
- The paper's goal is to detect interactions without compromising the functional forms. Our method accomplishes higher-order interaction detection, which has the benefit of avoiding a high false positive or false discovery rate.<br />
<br />
2. Interpretability: A lot of work has also been done in this particular area and it can be divided it the following broad categories:<br />
* Feature Importance through Decomposition: Methods like Input Gradient(Sundararajan et al., 2017) learns the importance of features through a gradient-based approach similar to backpropagation. Works like Li et al(2017), Murdoch(2017) and Murdoch(2018) study interpretability of LSTMs by looking at phrase and word level importance scores. Bach et al. 2015 and Shrikumar et al. 2016 (DeepLift) study pixel importance in CNNs.<br />
* Studying Visualizations in Models - Karpathy et al. (2015) worked with character generating LSTMs and tried to study activation and firing in certain hidden units for meaningful attributes. (Yosinski et al., 2015 studies feature map visualizations. <br />
* Attention-Based Models: Bahdanau et al. (2014) - These are a different class of models which use attention modules(different architectures) to help focus the neural network to decide the parts of the input that it should look more closely or give more importance to. Looking at the results of these type of model an indirect sense of interpretability can be gauged.<br />
<br />
The approach in this paper is to extract non-additive interactions between variables from the neural network weights.<br />
<br />
=Notations=<br />
Before we dive in to methodology, we are going to define a few notations here. Most of them will be trivial.<br />
<br />
1. Vector: Vectors are defined with bold-lowercases, '''v, w'''<br />
<br />
2. Matrix: Matrice are defined with blod-uppercases, '''V, W'''<br />
<br />
3. Interger Set: For some interger p <math>\in</math> Z, we define [p] := {1,2,3,...,p}<br />
<br />
=Interaction=<br />
First of all, in order to explain the model, we need to be able to explain the interactions and their effects to output. Therefore, we define 'interacion' between variables as below. <br />
<br />
[[File:def_interaction.PNG|900px|center]]<br />
<br />
From the definition above, for a function like, <math>x_1x_2 + sin(x_3 + x_4 + x_5)</math>, we have <math>{[x_1, x_2]}</math> and <math>{[x_3, x_4, x_5]}</math> interactions. And we say that the latter interaction to be 3-way interaction.<br />
<br />
Note that from the definition above, we can naturally deduce that d-way interaction can exist if and only if all of its (d-1) interactions exist. For example, 3-way interaction above shows that we have 2-way interactions <math>{[3,4], [4,5]}</math> and <math>{[3,5]}</math>.<br />
<br />
One thing that we need to keep in mind is that for models like neural network, most of interactions are happening within hidden layers. This means that we needa proper way of measuring interaction strength.<br />
<br />
The key observation is that for any kinds of interaction, at a some hidden unit of some hidden layer, two interacting features the ancestors. In graph-theoretical language, interaction map can be viewed as an associated directed graph and for any interaction <math>\Gamma \in [p]</math>, there exists at least one vertix that has all of features of <math>\Gamma</math> as ancestors. The statement can be rigorized as the following:<br />
<br />
<br />
[[File:prop2.PNG|900px|center]]<br />
<br />
Now, the above mathematical statement gurantees us to measure interaction strengths at ANY hidden layers. For example, if we want to study about interactions at some specific hidden layer, now we now that there exists corresponding vertices between the hidden layer and output layer. Therefore all we need to do is now to find approprite measure which can summarize the information between those two layers.<br />
<br />
Before doing so, let's think about a single-layered neural network. For any one hidden unit, we can have possibly, <math>2^{||W_i,:||}</math>, number of interactions. This means that our search space might be too huge for multi-layered networks. Therefore, we need a some descent way of approximate out search space. Moreover, the authors realized a fast interaction detection by limiting the search complexity of the task by only quantifying interactions created at the first hidden layer. The figure below illustrates an interaction within a fully connected feedforward neural network, where the box contains later layers in the network.<br />
<br />
[[File:network1.PNG|500px|center]]<br />
<br />
==Measuring influence in hidden layers==<br />
As we discussed above, in order to consider interaction between units in any layers, we need to think about their out-going paths. However, we soon encountered the fact that for some fully-connected multi-layer neural network, the search space might be too huge to compare. Therefore, we use information about out-going paths gredient upper bond. To represent the influence of out-going paths at <math>l</math>-hidden layer, we define cumulative impact of weights between output layer and <math>l+1</math>. We define aggregated weights as, <br />
<br />
[[File:def3.PNG|900px|center]]<br />
<br />
<br />
Note that <math>z^{(l)} \in R^{(p_l)}</math> where <math>p_l</math> is the number of hidden units in <math>l</math>-layer.<br />
Moreover, this is the lipschitz constant of gredients. Gredient has been an import variable of measuring influence of features, especially when we consider that input layer's derivative computes the direction normal to decision boundaries.<br />
<br />
==Quantifying influence==<br />
For some <math>i</math> hidden unit at the first hidden layer, which is the closet layer to the input layer, we define the influence strength of some interaction as, <br />
<br />
[[File:measure1.PNG|900px|center]]<br />
<br />
The function <math>\mu</math> will be defined later. Essentially, the formula shows that the strength of influence is defined as the product of the aggregated weight on the first hidden layer and some measure of influence between the first hidden layer and the input layer. <br />
<br />
For the function, <math>\mu</math>, any positive-real valued functions such as max, min and average can be candidates. The effects of those candidates will be tested later.<br />
<br />
Now based on the specifications above, the author suggested the algorithm for searching influential interactions between input layer units as follows:<br />
<br />
It was pointed out that restricting to the first hidden layer might miss some important feature interactions, however, the author state that it is not straightforward how to incorporate the idea of hidden units at intermediate layers to get better interaction detection performance.<br />
[[File:algorithm1.PNG|850px|center]]<br />
<br />
=Cut-off Model=<br />
Now using the greedy algorithm defined above, we can rank the interactions by their strength. However, in order to access true interactions, we are building the cut-off model which is a generalized additive model (GAM) as below,<br />
<br />
<center><math><br />
c_K('''x''') = \sum_{i=1}^{p}g_i(x_i) + \sum_{i=1}^{K}{g_i}^\prime(x_\chi)<br />
</math></center><br />
<br />
From the above model, each <math>g</math> and <math>g^*</math> are Feed-Forward neural network. We are keep adding interactions until the performance reaches plateaus.<br />
<br />
=Experiment=<br />
For the experiment, the authors have compared three neural network model with traditional statistical interaction detecting algorithms. For the nueral network models, first model will be MLP, second model will be MLP-M, which is MLP with additional univariate network at the output. The last one is the cut-off model defined above, which is denoted by MLP-cutoff. In the experiments that the authors performed, all the networks which modelled feature interactions consisted of four hidden layers containing 140, 100, 60, and 20 units respectively. Whereas, all the individual univariate networks contained three hidden layers with each layer containing 10 units. All of these networks used ReLu activation and backpropagation for training. The MLP-M model is graphically represented below.<br />
<br />
[[File:output11.PNG|300px|center]]<br />
<br />
For the experiment, the authors study our interaction detection framework on both simulated and real-world experiments. For simulated experiments, the authors are going to test on 10 synthetic functions as shown in table I.<br />
<br />
[[File:synthetic.PNG|900px|center]]<br />
<br />
The authors use four real-world datasets, of which two are regression datasets, and the other two are binary classification datasets. The datasets are a mixture of common prediction tasks in the cal housing<br />
and bike sharing datasets, a scientific discovery task in the higgs boson dataset, and an example of very-high order interaction detection in the letter dataset.<br />
<br />
And the authors also reported the results of comparisons between the models. As you can see, neural network based models are performing better on average. Compare to the traditional methods like ANOVA, MLP and MLP-M method shows 20% increases in performance.<br />
<br />
[[File:performance_mlpm.PNG|900px|center]]<br />
<br />
<br />
[[File:performance2_mlpm.PNG|900px|center]]<br />
<br />
The above result shows that MLP-M almost perfectly capture the most influential pair-wise interactions.<br />
<br />
=Highe-order interatcion detection=<br />
The authors use their greedy interaction ranking algorithm to perform higher-order interactiondetection without an exponential search of interaction candidates.<br />
[[File:higher-order_interaction_detection.png|700px|center]]<br />
<br />
=Limitations=<br />
Even though for the above synthetic experiment MLP methods showed superior performances, the method still have some limitations. For example, fir the function like, <math>x_1x_2 + x_2x_3 + x_1x_3</math>, neural network fails to distinguish between interlinked interactions to single higher order interaction. Moreoever, correlation between features deteriorates the ability of the network to distinguish interactions. However, correlation issues are presented most of interaction detection algorithms. <br />
<br />
Because this method relies on the neural network fitting the data well, there are some additional concerns. Notably, if the NN is unable to make an appropriate fit (under/overfitting), the resulting interactions will be flawed. This can occur if the datasets that are too small or too noisy, which often occurs in practical settings. <br />
<br />
=Conclusion=<br />
Here we presented the method of detecting interactions using MLP. Compared to other state-of-the-art methods like Additive Groves (AG), the performances are competitive yet computational powers required is far less. Therefore, it is safe to claim that the method will be extremly useful for practitioners with (comparably) less computational powers. Moreover, the NIP algorithm successfully reduced the computation sizes. After all, the most important aspect of this algorithm is that now users of nueral networks can impose interpretability in the model usage, which will change the level of usability to another level for most of practitioners outside of those working in machine learning and deep learning areas.<br />
<br />
For future work, the authors want to detect feature interactions by using the common units in the intermediate hidden layers of feedforward networks, and also want to use such interaction detection to interpret weights in other deep neural networks. Also, it was pointed out that the neural network weights heavily depend on L-1 regularized neural network training, but a group lasso penalty may work better.<br />
<br />
=Critique=<br />
1. Authors need to do large-scale experiments, instead of just conducting experiments on some synthetic dataset with small feature dimensionality, to make their claim stronger.<br />
<br />
2. Although the method proposed in this paper is interesting, the paper would benefit from providing some more explanations to support its idea and fill the possible gaps in its experimental evaluation. In some parts there are repetitive explanations that could be replaced by other essential clarifications.<br />
<br />
3. Greedy algorithm is implemented but nothing is mentioned about the speed of this algorithm which is definitely not fast. So, this has the potential to be a weak point of the study.<br />
<br />
=Reference=<br />
<br />
[1] Jacob Bien, Jonathan Taylor, and Robert Tibshirani. A lasso for hierarchical interactions. Annals of statistics, 41(3):1111, 2013. <br />
<br />
[2] G David Garson. Interpreting neural-network connection weights. AI Expert, 6(4):46–51, 1991.<br />
<br />
[3] Yotam Hechtlinger. Interpretation of prediction models using the input gradient. arXiv preprint arXiv:1611.07634, 2016.<br />
<br />
[4] Shiyu Liang and R Srikant. Why deep neural networks for function approximation? 2016. <br />
<br />
[5] David Rolnick and Max Tegmark. The power of deeper networks for expressing natural functions. International Conference on Learning Representations, 2018. <br />
<br />
[6] Daria Sorokina, Rich Caruana, and Mirek Riedewald. Additive groves of regression trees. Machine Learning: ECML 2007, pp. 323–334, 2007.<br />
<br />
[7] Simon Wood. Generalized additive models: an introduction with R. CRC press, 2006<br />
<br />
[8] Sebastian Bach, Alexander Binder, Gre ́goire Montavon, Frederick Klauschen, Klaus-Robert Mu ̈ller, and Wojciech Samek. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS one, 10(7):e0130140, 2015.<br />
<br />
[9] Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad. Intel- ligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1721–1730. ACM, 2015.<br />
<br />
[10] Zhengping Che, Sanjay Purushotham, Robinder Khemani, and Yan Liu. Interpretable deep models for icu outcome prediction. In AMIA Annual Symposium Proceedings, volume 2016, pp. 371. American Medical Informatics Association, 2016.<br />
<br />
[11] Laurent Itti, Christof Koch, and Ernst Niebur. A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on pattern analysis and machine intelligence, 20(11):1254– 1259, 1998.<br />
<br />
[12] Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. Why should i trust you?: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144. ACM, 2016.<br />
<br />
[13]Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. Deep inside convolutional networks: Vi- sualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034, 2013.<br />
<br />
[14] Kush R Varshney and Homa Alemzadeh. On the safety of machine learning: Cyber-physical sys- tems, decision sciences, and data products. arXiv preprint arXiv:1610.01256, 2016.<br />
<br />
[15] Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson. Understanding neural networks through deep visualization. arXiv preprint arXiv:1506.06579, 2015.</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:higher-order_interaction_detection.png&diff=42090File:higher-order interaction detection.png2018-11-30T17:18:39Z<p>Aghabuss: </p>
<hr />
<div></div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Fix_your_classifier:_the_marginal_value_of_training_the_last_weight_layer&diff=42088Fix your classifier: the marginal value of training the last weight layer2018-11-30T17:12:42Z<p>Aghabuss: </p>
<hr />
<div><br />
The code for the proposed model is available at https://github.com/eladhoffer/fix_your_classifier.<br />
<br />
=Introduction=<br />
<br />
Deep neural networks have become widely used for machine learning, achieving state-of-the-art results on many tasks. One of the most common tasks they are used for is classification. For example, convolutional neural networks (CNNs) are used to classify images to a semantic category. Typically, a learned affine transformation is placed at the end of such models, yielding a per-class value used for classification. This classifier can have a vast number of parameters, which grows linearly with the number of possible classes, thus requiring increasingly more computational resources.<br />
<br />
=Brief Overview=<br />
<br />
In order to alleviate the aforementioned problem, the authors propose that the final layer of the classifier be fixed (up to a global scale constant). They argue that with little or no loss of accuracy for most classification tasks, the method provides significant memory and computational benefits. In addition, they show that by initializing the classifier with a Hadamard matrix the inference could be made faster as well.<br />
<br />
=Previous Work=<br />
<br />
Training NN models and using them for inference requires large amounts of memory and computational resources; thus, extensive amount of research has been done lately to reduce the size of networks which are as follows:<br />
<br />
* Weight sharing and specification (Han et al., 2015)<br />
<br />
* Mixed precision to reduce the size of the neural networks by half (Micikevicius et al., 2017)<br />
<br />
* Low-rank approximations to speed up CNN (Tai et al., 2015)<br />
<br />
* Quantization of weights, activations, and gradients to further reduce computation during training (Hubara et al., 2016b; Li et al., 2016 and Zhou et al., 2016)<br />
<br />
Some of the past works have also put forward the fact that predefined (Park & Sandberg, 1991) and random (Huang et al., 2006) projections can be used together with a learned affine transformation to achieve competitive results on many of the classification tasks. However, the authors' proposal in the current paper is quite reversed.<br />
<br />
=Background=<br />
<br />
A Convolutional Neural Network (CNN) is comprised of one or more convolutional layers (often with a subsampling step) and then followed by one or more fully connected layers as in a standard multilayer neural network. The architecture of a CNN is designed to take advantage of the 2D structure of an input image (or other 2D input such as a speech signal). This is achieved with local connections and tied weights followed by some form of pooling which results in translation invariant features. Another benefit of CNNs is that they are easier to train and have many fewer parameters than fully connected networks with the same number of hidden units. <br />
<br />
A CNN consists of a number of convolutional and subsampling layers optionally followed by fully connected layers. The input to a convolutional layer is a <math>m \times m \times r</math> image where m is the height and width of the image and <math>r</math> is the number of channels, e.g. an RGB image has <math>r=3</math>. The convolutional layer will have <math>k</math> filters (or kernels) of size <math>n \times n \times q</math> where <math>n</math> is smaller than the dimension of the image and <math>q</math> can either be the same as the number of channels <math>r</math> or smaller and may vary for each kernel. The size of the filters gives rise to the locally connected structure which are each convolved with the image to produce <math>k</math> feature maps of size <math>m−n+1</math>. Each map is then subsampled typically with mean or max pooling over <math>p \times p</math> contiguous regions where <math>p</math> ranges between 2 for small images (e.g. MNIST) and is usually not more than 5 for larger inputs. Either before or after the subsampling layer an additive bias and sigmoidal nonlinearity is applied to each feature map. <br />
<br />
CNNs are commonly used to solve a variety of spatial and temporal tasks. Earlier architectures of CNNs (LeCun et al., 1998; Krizhevsky et al., 2012) used a set of fully-connected layers at later stages of the network, presumably to allow classification based on global features of an image.<br />
<br />
<br />
<br />
== Shortcomings of the Final Classification Layer and its Solution ==<br />
<br />
Zeiler & Fergus, 2014 showed that despite the enormous number of trainable parameters these layers add to the model, they are known to have a rather marginal impact on the final performance of the network.<br />
<br />
It has been shown previously that these layers could be easily compressed and reduced after a model was trained by simple means such as matrix decomposition and sparsification (Han et al., 2015). Modern architecture choices are characterized with the removal of most of the fully connected layers (Lin et al., 2013; Szegedy et al., 2015; He et al., 2016), that lead to better generalization and overall accuracy, together with a huge decrease in the number of trainable parameters. Additionally, numerous works showed that CNNs can be trained in a metric learning regime (Bromley et al., 1994; Schroff et al., 2015; Hoffer & Ailon, 2015), where no explicit classification layer was introduced and the objective regarded only distance measures between intermediate representations. Hardt & Ma (2017) suggested an all-convolutional network variant, where they kept the original initialization of the classification layer fixed with no negative impact on performance on the CIFAR-10 dataset.<br />
<br />
=Proposed Method=<br />
<br />
The aforementioned works provide evidence that fully-connected layers are in fact redundant and play a small role in learning and generalization. In this work, the authors have suggested that the parameters used for the final classification transform are completely redundant, and can be replaced with a predetermined linear transform. This holds for even in large-scale models and classification tasks, such as recent architectures trained on the ImageNet benchmark (Deng et al., 2009).<br />
<br />
==Using a Fixed Classifier==<br />
<br />
Suppose the final representation obtained by the network (the last hidden layer) is represented as <math>x = F(z;\theta)</math> where <math>F</math> is assumed to be a deep neural network with input z and parameters θ, e.g., a convolutional network, trained by backpropagation.<br />
<br />
In common NN models, this representation is followed by an additional affine transformation, <math>y = W^T x + b</math> ,where <math>W</math> and <math>b</math> are also trained by back-propagation.<br />
<br />
For input <math>x</math> of <math>N</math> length, and <math>C</math> different possible outputs, <math>W</math> is required to be a matrix of <math>N ×<br />
C</math>. Training is done using cross-entropy loss, by feeding the network outputs through a softmax activation<br />
<br />
<math><br />
v_i = \frac{e^{y_i}}{\sum_{j}^{C}{e^{y_j}}}, i &isin; </math> { <math> {1, . . . , C} </math> }<br />
<br />
and reducing the expected negative log likelihood with respect to ground-truth target <math> t &isin; </math> { <math> {1, . . . , C} </math> },<br />
by minimizing the loss function:<br />
<br />
<math><br />
L(x, t) = −\text{log}\ {v_t} = −{w_t}·{x} − b_t + \text{log} ({\sum_{j}^{C}e^{w_j . x + b_j}})<br />
</math><br />
<br />
where <math>w_i</math> is the <math>i</math>-th column of <math>W</math>.<br />
<br />
==Choosing the Projection Matrix==<br />
<br />
To evaluate the conjecture regarding the importance of the final classification transformation, the trainable parameter matrix <math>W</math> is replaced with a fixed orthonormal projection <math> Q &isin; R^{N×C} </math>, such that <math> &forall; i &ne; j : q_i · q_j = 0 </math> and <math> || q_i ||_{2} = 1 </math>, where <math>q_i</math> is the <math>i</math>th column of <math>Q</math>. This is ensured by a simple random sampling and singular-value decomposition<br />
<br />
As the rows of classifier weight matrix are fixed with an equally valued <math>L_{2}</math> norm, we find it beneficial<br />
to also restrict the representation of <math>x</math> by normalizing it to reside on the <math>n</math>-dimensional sphere:<br />
<br />
<center><math><br />
\hat{x} = \frac{x}{||x||_{2}}<br />
</math></center><br />
<br />
This allows faster training and convergence, as the network does not need to account for changes in the scale of its weights. However, it has now an issue that <math>q_i · \hat{x} </math> is bounded between −1 and 1. This causes convergence issues, as the softmax function is scale sensitive, and the network is affected by the inability to re-scale its input. This issue is amended with a fixed scale <math>T</math> applied to softmax inputs <math>f(y) = softmax(\frac{1}{T}y)</math>, also known as the ''softmax temperature''. However, this introduces an additional hyper-parameter which may differ between networks and datasets. So, the authors propose to introduce a single scalar parameter <math>\alpha</math> to learn the softmax scale, effectively functioning as an inverse of the softmax temperature <math>\frac{1}{T}</math>. The normalized weights and an additional scale coefficient are also used, specially using a single scale for all entries in the weight matrix. The additional vector of bias parameters <math>b &isin; \mathbb{R}^{C}</math> is kept the same and the model is trained using the traditional negative-log-likelihood criterion. Explicitly, the classifier output is now:<br />
<br />
<center><br />
<math><br />
v_i=\frac{e^{\alpha q_i &middot; \hat{x} + b_i}}{\sum_{j}^{C} e^{\alpha q_j &middot; \hat{x} + b_j}}, i &isin; </math> { <math> {1,...,C} </math>}<br />
</center><br />
<br />
and the loss to be minimized is:<br />
<br />
<center><math><br />
L(x, t) = -\alpha q_t &middot; \frac{x}{||x||_{2}} + b_t + \text{log} (\sum_{i=1}^{C} \text{exp}((\alpha q_i &middot; \frac{x}{||x||_{2}} + b_i)))<br />
</math></center><br />
<br />
where <math>x</math> is the final representation obtained by the network for a specific sample, and <math> t &isin; </math> { <math> {1, . . . , C} </math> } is the ground-truth label for that sample. The behaviour of the parameter <math> \alpha </math> over time, which is logarithmic in nature and has the same behavior exhibited by the norm of a learned classifier, is shown in<br />
[[Media: figure1_log_behave.png| Figure 1]].<br />
<br />
<center>[[File:figure1_log_behave.png]]</center><br />
<br />
When <math> -1 \le q_i · \hat{x} \le 1 </math>, a possible cosine angle loss is <br />
<br />
<center>[[File:caloss.png]]</center><br />
<br />
But its final validation accuracy has a slight decrease, compared to original models.<br />
<br />
==Using a Hadmard Matrix==<br />
<br />
To recall, Hadmard matrix (Hedayat et al., 1978) <math> H </math> is an <math> n × n </math> matrix, where all of its entries are either +1 or −1.<br />
Furthermore, <math> H </math> is orthogonal, such that <math> HH^{T} = nI_n </math> where <math>I_n</math> is the identity matrix. Instead of using the entire Hadmard matrix <math>H</math>, a truncated version, <math> \hat{H} &isin; </math> {<math> {-1, 1}</math>}<math>^{C \times N}</math> where all <math>C</math> rows are orthogonal as the final classification layer is such that:<br />
<br />
<center><math><br />
y = \hat{H} \hat{x} + b<br />
</math></center><br />
<br />
This usage allows two main benefits:<br />
* A deterministic, low-memory and easily generated matrix that can be used for classification.<br />
* Removal of the need to perform a full matrix-matrix multiplication - as multiplying by a Hadamard matrix can be done by simple sign manipulation and addition.<br />
<br />
Here, <math>n</math> must be a multiple of 4, but it can be easily truncated to fit normally defined networks. Also, as the classifier weights are fixed to need only 1-bit precision, it is now possible to focus our attention on the features preceding it.<br />
<br />
=Experimental Results=<br />
<br />
The authors have evaluated their proposed model on the following datasets:<br />
<br />
==CIFAR-10/100==<br />
<br />
===About the Dataset===<br />
<br />
CIFAR-10 is an image classification benchmark dataset containing 50,000 training images and 10,000 test images. The images are in color and contain 32×32 pixels. There are 10 possible classes of various animals and vehicles. CIFAR-100 holds the same number of images of the same size, but contains 100 different classes.<br />
<br />
===Training Details===<br />
<br />
The authors trained a residual network ( He et al., 2016) on the CIFAR-10 dataset. The network depth was 56 and the same hyper-parameters as in the original work were used. A comparison of the two variants, i.e., the learned classifier and the proposed classifier with a fixed transformation is shown in [[Media: figure1_resnet_cifar10.png | Figure 2]].<br />
<br />
<center>[[File: figure1_resnet_cifar10.png]]</center><br />
<br />
These results demonstrate that although the training error is considerably lower for the network with learned classifier, both models achieve the same classification accuracy on the validation set. The authors' conjecture is that with the new fixed parameterization, the network can no longer increase the norm of a given sample’s representation - thus learning its label requires more effort. As this may happen for specific seen samples - it affects only training error.<br />
<br />
The authors also compared using a fixed scale variable <math>\alpha </math> at different values vs. the learned parameter. Results for <math> \alpha = </math> {0.1, 1, 10} are depicted in [[Media: figure3_alpha_resnet_cifar.png| Figure 3]] for both training and validation error and as can be seen, similar validation accuracy can be obtained using a fixed scale value (in this case <math>\alpha </math>= 1 or 10 will suffice) at the expense of another hyper-parameter to seek. In all the further experiments the scaling parameter <math> \alpha </math> was regularized with the same weight decay coefficient used on original classifier. Although learning the scale is not necessary, but it will help convergence during training.<br />
<br />
<center>[[File: figure3_alpha_resnet_cifar.png]]</center><br />
<br />
The authors then train the model on CIFAR-100 dataset. They used the DenseNet-BC model from Huang et al. (2017) with a depth of 100 layers and k = 12. The higher number of classes caused the number of parameters to grow and encompassed about 4% of the whole model. However, validation accuracy for the fixed-classifier model remained equally good as the original model, and the same training curve was observed as earlier.<br />
<br />
==IMAGENET==<br />
<br />
===About the Dataset===<br />
<br />
The Imagenet dataset introduced by Deng et al. (2009) spans over 1000 visual classes, and over 1.2 million samples. This is supposedly a more challenging dataset to work on as compared to CIFAR-10/100.<br />
<br />
===Experiment Details===<br />
<br />
The authors evaluated their fixed classifier method on Imagenet using Resnet50 by He et al. (2016) and Densenet169 model (Huang et al., 2017) as described in the original work. Using a fixed classifier removed approximately 2-million parameters were from the model, accounting for about 8% and 12 % of the model parameters respectively. The experiments revealed similar trends as observed on CIFAR-10.<br />
<br />
For a more stricter evaluation, the authors also trained a Shufflenet architecture (Zhang et al., 2017b), which was designed to be used in low memory and limited computing platforms and has parameters making up the majority of the model. They were able to reduce the parameters to 0.86 million as compared to 0.96 million parameters in the final layer of the original model. Again, the proposed modification in the original model gave similar convergence results on validation accuracy. Interestingly, this method allowed Imagenet training in an under-specified regime, where there are<br />
more training samples than the number of parameters. This is an unconventional regime for modern deep networks, which are usually over-specified to have many more parameters than training samples (Zhang et al., 2017a).<br />
<br />
The overall results of the fixed-classifier are summarized in [[Media: table1_fixed_results.png | Table 1]].<br />
<br />
<center>[[File: table1_fixed_results.png]]</center><br />
<br />
==Language Modelling==<br />
<br />
Recent works have empirically found that using the same weights for both word embedding and classifier can yield equal or better results than using a separate pair of weights. So the authors experimented with fix-classifiers on language modeling as it also requires classification of all possible tokens available in the task vocabulary. They trained a recurrent model with 2-layers of LSTM (Hochreiter & Schmidhuber, 1997) and embedding + hidden size of 512 on the WikiText2 dataset (Merity et al., 2016), using same settings as in Merity et al. (2017). WikiText2 dataset contains about 33K different words, so the number of parameters expected in the embedding and classifier layer was about 34-million. This number is about 89% of the total number of parameters used for the whole model which is 38-million. However, using a random orthogonal transform yielded poor results compared to learned embedding. This was suspected to be due to semantic relationships captured in the embedding layer of language models, which is not the case in image classification task. The intuition was further confirmed by the much better results when pre-trained embeddings using word2vec algorithm by Mikolov et al. (2013) or PMI factorization as suggested by Levy & Goldberg (2014), were used.<br />
<br />
<center>[[File: language.png]]</center><br />
<br />
=Discussion=<br />
<br />
==Implications and Use Cases==<br />
<br />
With the increasing number of classes in the benchmark datasets, computational demands for the final classifier will increase as well. In order to understand the problem better, the authors observe the work by Sun et al. (2017), which introduced JFT-300M - an internal Google dataset with over 18K different classes. Using a Resnet50 (He et al., 2016), with a 2048 sized representation led to a model with over 36M parameters meaning that over 60% of the model parameters resided in the final classification layer. Sun et al. (2017) also describe the difficulty in distributing so many parameters over the training servers involving a non-trivial overhead during synchronization of the model for update. The authors claim that the fixed-classifier would help considerably in this kind of scenario - where using a fixed classifier removes the need to do any gradient synchronization for the final layer. Furthermore, introduction of Hadamard matrix removes the need to save the transformation altogether, thereby, making it more efficient and allowing considerable memory and computational savings.<br />
<br />
==Possible Caveats==<br />
<br />
The good performance of fixed-classifiers relies on the ability of the preceding layers to learn separable representations. This could be affected when the ratio between learned features and number of classes is small – that is, when <math> C > N</math>. However, they tested their method in such cases and their model performed well and provided good results.<br />
Another factor that can affect the performance of their model using a fixed classifier is when the classes are highly correlated. In that case, the fixed classifier actually cannot support correlated classes and thus, the network could have some difficulty to learn. For a language model, word classes tend to have highly correlated instances, which also lead to difficult learning process.<br />
<br />
Also, this proposed approach will only eliminate the computation of the classifier weights, so when the classes are fewer, the computation saving effect will not be readily apparent.<br />
<br />
==Future Work==<br />
<br />
<br />
The use of fixed classifiers might be further simplified in Binarized Neural Networks (Hubara et al., 2016a), where the activations and weights are restricted to ±1 during propagations. In that case, the norm of the last hidden layer would be constant for all samples (equal to the square root of the hidden layer width). The constant could then be absorbed into the scale constant <math>\alpha</math>, and there is no need in a per-sample normalization.<br />
<br />
Additionally, more efficient ways to learn a word embedding should also be explored where similar redundancy in classifier weights may suggest simpler forms of token representations - such as low-rank or sparse versions.<br />
<br />
A related paper was published that claims that fixing most of the parameters of the neural network achieves comparable results with learning all of them [A. Rosenfeld and J. K. Tsotsos]<br />
<br />
=Conclusion=<br />
<br />
In this work, the authors argue that the final classification layer in deep neural networks is redundant and suggest removing the parameters from the classification layer. The empirical results from experiments on the CIFAR and IMAGENET datasets suggest that such a change lead to little or almost no decline in the performance of the architecture. Furthermore, using a Hadmard matrix as classifier might lead to some computational benefits when properly implemented, and save memory otherwise spent on large amount of transformation coefficients.<br />
<br />
Another possible scope of research that could be pointed out for future could be to find new efficient methods to create pre-defined word embeddings, which require huge amount of parameters that can possibly be avoided when learning a new task. Therefore, more emphasis should be given to the representations learned by the non-linear parts of the neural networks - up to the final classifier, as it seems highly redundant.<br />
<br />
=Critique=<br />
<br />
The paper proposes an interesting idea that has a potential use case when designing memory-efficient neural networks. The experiments shown in the paper are quite rigorous and provide support to the authors' claim. However, it would have been more helpful if the authors had described a bit more about efficient implementation of the Hadamard matrix and how to scale this method for larger datasets (cases with <math> C >N</math>).<br />
<br />
Moreover, one of the main intuitions of the paper has introduced to be computational cost but it has left out to compare a fixed and learned classifier based on the computational cost and then investigate whether it worth the drop in performance or not considering the fact that not always the output can be degraded because of need for speed! At least a discussion on this issue is expected.<br />
<br />
On the other hand, the computational cost and performance change after fixation of classifier could be related to dataset and the nature and complexity of it. Mostly, having 1000 classes makes the classification more crucial than 2 classes. An evaluation of this topic is also needed.<br />
<br />
Another interesting experiment to do would be to look this technique interacts with distillation when used in the teacher or student network or both. For instance, Does fixing the features make it more difficult to place dog than on boat when classifying a cat? Do networks with fixed classifier weights make worse teachers for distillation?<br />
<br />
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A. Rosenfeld and J. K. Tsotsos, “Intriguing properties of randomly weighted networks: Generalizing while learning next to nothing,” arXiv preprint arXiv:1802.00844, 2018.</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=learn_what_not_to_learn&diff=42086learn what not to learn2018-11-30T17:06:35Z<p>Aghabuss: /* Experiments */</p>
<hr />
<div>=Introduction=<br />
<br />
In reinforcement learning, it is often difficult for an agent to learn when the action space is large, especially the difficulties from function approximation and exploration. In some cases many actions are irrelevant and it is sometimes easier for the algorithm to learn which action not to take. The paper proposes a new reinforcement learning approach for dealing with large action spaces based on action elimination by restricting the available actions in each state to a subset of the most likely ones. There is a core assumption being made in the proposed method that it is easier to predict which actions in each state are invalid or inferior and use that information for control. More specifically, it proposes a system that learns the approximation of a Q-function and concurrently learns to eliminate actions. The method utilizes an external elimination signal which incorporates domain-specific prior knowledge. For example, in parser-based text games, the parser gives feedback regarding irrelevant actions after the action is played (e.g., Player: "Climb the tree." Parser: "There are no trees to climb"). Then a machine learning model can be trained to generalize to unseen states. <br />
<br />
The paper focuses on tasks where both states and the actions are natural language. It introduces a novel deep reinforcement learning approach which has a Deep Q-Network (DQN) and an Action Elimination Network (AEN), both using the Convolutional Neural Networks (CNN) for Natural Language Processing (NLP) tasks. The AEN is trained to predict invalid actions, supervised by the elimination signal from the environment. The proposed method uses the final layer activations of AEN to build a linear contextual bandit model which allows the elimination of sub-optimal actions with high probability. '''Note that the core assumption is that it is easy to predict which actions are invalid or inferior in each state and leverage that information for control.'''<br />
<br />
The text-based game called "Zork", which lets players to interact with a virtual world through a text-based interface is tested by using the elimination framework. <br />
In this game, the player explores an environment using imagination of the text he/she reads. For more info, you can watch this video: [https://www.youtube.com/watch?v=xzUagi41Wo0 Zork].<br />
<br />
The AEN algorithm has achieved a faster learning rate than the baseline agents by eliminating irrelevant actions.<br />
<br />
Below shows an example for the Zork interface:<br />
<br />
[[File:lnottol_fig1.png|500px|center]]<br />
<br />
All states and actions are given in natural language. Input for the game contains more than a thousand possible actions in each state since the player can type anything.<br />
<br />
=Related Work=<br />
<br />
Text-Based Games(TBG): The state of the environment in TBG is described by simple language. The player interacts with the environment with text command which respects a pre-defined grammar. A popular example is Zork which has been tested in the paper. TBG is a good research intersection of RL and NLP, it requires language understanding, long-term memory, planning, exploration, affordability extraction, and common sense. It also often introduce stochastic dynamics to increase randomness.<br />
<br />
Representations for TBG: Good word representation is necessary in order to learn control policies from high-dimensional complex data such as text. Previous work on TBG used pre-trained embeddings directly for control, other works combined pre-trained embedding with neural networks. For example, He<br />
et al. (2015) proposed to consider an input as Bag Of Words features for a neural network, learned separately<br />
embeddings for states and actions, and then computed the Q function from autocorrelations between<br />
these embeddings.<br />
<br />
DRL with linear function approximation: DRL methods such as the DQN have achieved state-of-the-art results in a variety of challenging, high-dimensional domains. This is mainly because neural networks can learn rich domain representations for value function and policy. On the other hand, linear representation batch reinforcement learning methods are more stable and accurate, while feature engineering is necessary.<br />
<br />
RL in Large Action Spaces: Prior work concentrated on factorizing the action space into binary subspace(Pazis and Parr, 2011; Dulac-Arnold et al., 2012; Lagoudakis and Parr, 2003), other works proposed to embed the discrete actions into a continuous space, then choose the nearest discrete action according to the optimal actions in the continuous space(Dulac-Arnold et al., 2015; Van Hasselt and Wiering, 2009). He et. al. (2015)extended DQN to unbounded(natural language) action spaces.<br />
Learning to eliminate actions was first mentioned by (Even-Dar, Mannor, and Mansour, 2003). They proposed to learn confidence intervals around the value function in each state. Lipton et al.(2016a) proposed to learn a classifier that detects hazardous state and then use it to shape the reward. Fulda et al.(2017) presented a method for affordability extraction via inner products of pre-trained word embedding.<br />
<br />
=Action Elimination=<br />
<br />
The approach in the paper builds on the standard Reinforcement Learning formulation. At each time step <math>t</math>, the agent observes state <math display="inline">s_t </math> and chooses a discrete action <math display="inline">a_t\in\{1,...,|A|\} </math>. Then, after action execution, the agent obtains a reward <math display="inline">r_t(s_t,a_t) </math> and observes next state <math display="inline">s_{t+1} </math> according to a transition kernel <math>P(s_{t+1}|s_t,a_t)</math>. The goal of the algorithm is to learn a policy <math display="inline">\pi(a|s) </math> which maximizes the expected future discounted cumulative return <math display="inline">V^\pi(s)=E^\pi[\sum_{t=0}^{\infty}\gamma^tr(s_t,a_t)|s_0=s]</math>, where <math> 0< \gamma <1 </math>. The Q-function is <math display="inline">Q^\pi(s,a)=E^\pi[\sum_{t=0}^{\infty}\gamma^tr(s_t,a_t)|s_0=s,a_0=a]</math>, and it can be optimized by Q-learning algorithm.<br />
<br />
After executing an action, the agent observes a binary elimination signal <math>e(s, a)</math> to determine which actions not to take. It equals 1 if action <math>a</math> may be eliminated in state <math>s</math> (and 0 otherwise). The signal helps mitigating the problem of large discrete action spaces. We start with the following definitions:<br />
<br />
'''Definition 1:''' <br />
<br />
Valid state-action pairs with respect to an elimination signal are state action pairs which the elimination process should not eliminate. <br />
<br />
The set of valid state-action pairs contains all of the state-action pairs that are a part of some optimal policy, i.e., only strictly suboptimal state-actions can be invalid.<br />
<br />
'''Definition 2:'''<br />
<br />
Admissible state-action pairs with respect to an elimination algorithm are state action pairs which the elimination algorithm does not eliminate.<br />
<br />
'''Definition 3:'''<br />
<br />
Action Elimination Q-learning is a Q-learning algorithm which updates only admissible state-action pairs and chooses the best action in the next state from its admissible actions. We allow the base Q-learning algorithm to be any algorithm that converges to <math display="inline">Q^*</math> with probability 1 after observing each state-action infinitely often.<br />
<br />
==Advantages of Action Elimination==<br />
<br />
The main advantage of action elimination is that it allows the agent to overcome some of the main difficulties in large action spaces which are Function Approximation and Sample Complexity. <br />
<br />
Function approximation: Errors in the Q-function estimates may cause the learning algorithm to converge to a suboptimal policy, this phenomenon becomes more noticeable when the action space is large. Action elimination mitigates this effect by taking the max operator only on valid actions, thus, reducing potential overestimation errors. Besides, by ignoring the invalid actions, the function approximation can also learn a simpler mapping (i.e., only the Q-values of the valid state-action pairs) leading to faster convergence and better solution.<br />
<br />
Sample complexity: The sample complexity measures the number of steps during learning, in which the policy is not <math display="inline">\epsilon</math>-optimal. Assume that there are <math>A'</math> actions that should be eliminated and are <math>\epsilon</math>-optimal, i.e. their value is at least <math>V^*(s)-\epsilon</math>. The invalid action often returns no reward and doesn't change the state, (Lattimore and Hutter, 2012)resulting in an action gap of <math display="inline">\epsilon=(1-\gamma)V^*(s)</math>, and this translates to <math display="inline">V^*(s)^{-2}(1-\gamma)^{-5}log(1/\delta)</math> wasted samples for learning each invalid state-action pair. Practically, elimination algorithm can eliminate these invalid actions and therefore speed up the learning process approximately by <math display="inline">A/A'</math>.<br />
<br />
Because it is difficult to embed the elimination signal into the MDP, the authors use contextual multi-armed bandits to decouple the elimination signal from the MDP, which can correctly eliminate actions when applying standard Q learning into learning process.<br />
<br />
==Action elimination with contextual bandits==<br />
<br />
Contextual bandit problem is a famous probability problem and is a natural extension from the multi-arm bandit problem.<br />
<br />
Let <math display="inline">x(s_t)\in R^d </math> be the feature representation of <math display="inline">s_t </math>. We assume that under this representation there exists a set of parameters <math display="inline">\theta_a^*\in \mathbb{R}^d </math> such that the elimination signal in state <math display="inline">s_t </math> is <math display="inline">e_t(s_t,a) = \theta_a^{*T}x(s_t)+\eta_t </math>, where <math display="inline"> \Vert\theta_a^*\Vert_2\leq S</math>. <math display="inline">\eta_t</math> is an R-subgaussian random variable with zero mean that models additive noise to the elimination signal. When there is no noise in the elimination signal, R=0. Otherwise, <math display="inline">R\leq 1</math> since the elimination signal is bounded in [0,1]. Assume the elimination signal satisfies: <math display="inline">0\leq E[e_t(s_t,a)]\leq l </math> for any valid action and <math display="inline"> u\leq E[e_t(s_t, a)]\leq 1</math> for any invalid action. And <math display="inline"> l\leq u</math>. Denote by <math display="inline">X_{t,a}</math> as the matrix whose rows are the observed state representation vectors in which action a was chosen, up to time t. <math display="inline">E_{t,a}</math> as the vector whose elements are the observed state representation elimination signals in which action a was chosen, up to time t. Denote the solution to the regularized linear regression <math display="inline">\Vert X_{t,a}\theta_{t,a}-E_{t,a}\Vert_2^2+\lambda\Vert \theta_{t,a}\Vert_2^2 </math> (for some <math display="inline">\lambda>0</math>) by <math display="inline">\hat{\theta}_{t,a}=\bar{V}_{t,a}^{-1}X_{t,a}^TE_{t,a} </math>, where <math display="inline">\bar{V}_{t,a}=\lambda I + X_{t,a}^TX_{t,a}</math>.<br />
<br />
<br />
According to Theorem 2 in (Abbasi-Yadkori, Pal, and Szepesvari, 2011), <math display="inline">|\hat{\theta}_{t,a}^{T}x(s_t)-\theta_a^{*T}x(s_t)|\leq\sqrt{\beta_t(\delta)x(s_t)^T\bar{V}_{t,a}^{-1}x(s_t)}\ \forall t>0</math>, where <math display="inline">\sqrt{\beta_t(\delta)}=R\sqrt{2\ \text{log}(\text{det}(\bar{V}_{t,a})^{1/2}\text{det}(\lambda I)^{-1/2}/\delta)}+\lambda^{1/2}S</math>, with probability of at least <math display="inline">1-\delta</math>. If <math display="inline">\forall s\ ,\Vert x(s)\Vert_2 \leq L</math>, then <math display="inline">\beta_t</math> can be bounded by <math display="inline">\sqrt{\beta_t(\delta)} \leq R \sqrt{d\ \text{log}(1+tL^2/\lambda/\delta)}+\lambda^{1/2}S</math>. Next, define <math display="inline">\tilde{\delta}=\delta/k</math> and bound this probability for all the actions. i.e., <math display="inline">\forall a,t>0</math><br />
<br />
<math display="inline">Pr(|\hat{\theta}_{t-1,a}^{T}x(s_t)-\theta_{t-1, a}^{*T}x(s_t)|\leq\sqrt{\beta_t(\tilde\delta)x(s_t)^T\bar{V}_{t - 1,a}^{-1}x(s_t)}) \leq 1-\delta</math><br />
<br />
Recall that <math display="inline">E[e_t(s,a)]=\theta_a^{*T}x(s_t)\leq l</math> if a is a valid action. Then we can eliminate action a at state <math display="inline">s_t</math> if it satisfies:<br />
<br />
<math display="inline">\hat{\theta}_{t-1,a}^{T}x(s_t)-\sqrt{\beta_{t-1}(\tilde\delta)x(s_t)^T\bar{V}_{t-1,a}^{-1}x(s_t)})>l</math><br />
<br />
with probability <math display="inline">1-\delta</math> that we never eliminate any valid action. Note that <math display="inline">l, u</math> are not known. In practice, choosing <math display="inline">l</math> to be 0.5 should suffice.<br />
<br />
==Concurrent Learning==<br />
In fact, Q-learning and contextual bandit algorithms can learn simultaneously, resulting in the convergence of both algorithms, i.e., finding an optimal policy and a minimal valid action space. <br />
<br />
If the elimination is done based on the concentration bounds of the linear contextual bandits, it can be ensured that Action Elimination Q-learning converges, as shown in Proposition 1.<br />
<br />
'''Proposition 1:'''<br />
<br />
Assume that all state action pairs (s,a) are visited infinitely often, unless eliminated according to <math display="inline">\hat{\theta}_{t-1,a}^Tx(s)-\sqrt{\beta_{t-1}(\tilde{\delta})x(s)^T\bar{V}_{t-1,a}^{-1}x(s))}>l</math>. Then, with a probability of at least <math display="inline">1-\delta</math>, action elimination Q-learning converges to the optimal Q-function for any valid state-action pairs. In addition, actions which should be eliminated are visited at most <math display="inline">T_{s,a}(t)\leq 4\beta_t/(u-l)^2<br />
+1</math> times.<br />
<br />
Notice that when there is no noise in the elimination signal(R=0), we correctly eliminate actions with probability 1. so invalid actions will be sampled a finite number of times.<br />
<br />
=Method=<br />
<br />
The assumption that <math display="inline">e_t(s_t,a)=\theta_a^{*T}x(s_t)+\eta_t </math> generally does not hold when using raw features like word2vec. So the paper proposes to use the neural network's last layer as feature representation of states. A practical challenge here is that the features must be fixed over time when used by the contextual bandit. So batch-updates framework(Levine et al., 2017;Riquelme, Tucker, and Snoek, 2018) is used, where a new contextual bandit model is learned for every few steps that uses the last layer activation of the AEN as features.<br />
<br />
==Architecture of action elimination framework==<br />
<br />
[[File:lnottol_fig1b.png|300px|center]]<br />
<br />
After taking action <math display="inline">a_t</math>, the agent observes <math display="inline">(r_t,s_{t+1},e_t)</math>. The agent uses it to learn two function approximation deep neural networks: A DQN and an AEN. AEN provides an admissible actions set <math display="inline">A'</math> to the DQN, which uses this set to decide how to act and learn. The architecture for both the AEN and DQN is an NLP CNN(100 convolutional filters for AEN and 500 for DQN, with three different 1D kernels of length (1,2,3)), based on(Kim, 2014). The state is represented as a sequence of words, composed of the game descriptor and the player's inventory. These are truncated or zero padded to a length of 50 descriptor + 15 inventory words and each word is embedded into continuous vectors using word2vec in <math display="inline">R^{300}</math>. The features of the last four states are then concatenated together such that the final state representations s are in <math display="inline">R^{78000}</math>. The AEN is trained to minimize the MSE loss, using the elimination signal as a label. The code, the Zork domain, and the implementation of the elimination signal can be found [https://github.com/TomZahavy/CB_AE_DQN here.]<br />
<br />
==Psuedocode of the Algorithm==<br />
<br />
[[File:lnottol_fig2.png|750px|center]]<br />
<br />
AE-DQN trains two networks: a DQN denoted by Q and an AEN denoted by E. The algorithm creates a linear contextual bandit model from it every L iterations with procedure AENUpdate(). This procedure uses the activations of the last hidden layer of E as features, which are then used to create a contextual linear bandit model.AENUpdate() then solved this model and plugin it into the target AEN. The contextual linear bandit model <math display="inline">(E^-,V)</math> is then used to eliminate actions via the ACT() and Target() functions. ACT() follows an <math display="inline">\epsilon</math>-greedy mechanism on the admissible actions set. For exploitation, it selects the action with highest Q-value by taking an argmax on Q-values among <math display="inline">A'</math>. For exploration, it selects an action uniformly from <math display="inline">A'</math>. The targets() procedure is estimating the value function by taking max over Q-values only among admissible actions, hence, reducing function approximation errors.<br />
<br />
=Experiments=<br />
==Grid Domain==<br />
The authors start by evaluating our algorithm on a small grid world domain with 9 rooms, where they ca analyze the effect of the action elimination (visualization can be found in the appendix). In this domain, the agent starts at the center of the grid and needs to navigate to its upper-left corner. On every step, the agent suffers a penalty of (−1), with a terminal reward of 0. Prior to the game, the states are randomly divided into K categories. The environment has 4K navigation actions, 4 for each category, each with a probability to move in a random direction. If the chosen action belongs to the same category as the state, the action is performed correctly in probability pTc = 0.75. Otherwise, it will be performed correctly in probability pFc = 0.5. If the action does not fit the state category, the elimination signal equals 1, and if the action and state belong to the same category, then e = 0. The optimal policy will only use the navigation actions from the same type as the state, and all of the other actions are strictly suboptimal. A basic comparison between vanilla Q-learning without action elimination (green) and a tabular version of the action elimination Q-learning (blue) can be found in the figure below. In all of the figures, the results are compared to the case with one category (red), i.e., only 4 basic navigation actions, which forms an upper bound on performance with multiple categories. In Figure (a),(c), the episode length is T = 150, and in Figure (b) it is T = 300, to allow sufficient exploration for the vanilla Q-Learning. It is clear from the simulations that the action elimination dramatically improves the results in large action spaces. Also, note that the gain from action elimination increases with the grid size since the elimination allows the agent to reach the goal earlier.<br />
<br />
<br />
[[File:griddomain.png|1200px|thumb|center|Performance of agents in grid world]]<br />
==Zork domain==<br />
<br />
The world of Zork presents a rich environment with a large state and action space. <br />
Zork players describe their actions using natural language instructions. For example, "open the mailbox". Then their actions were processed by a sophisticated natural language parser. Based on the results, the game presents the outcome of the action. The goal of Zork is to collect the Twenty Treasures of Zork and install them in the trophy case. Points that are generated from the game's scoring system are given to the agent as the reward. For example, the player gets the points when solving the puzzles. Placing all treasures in the trophy will get 350 points. The elimination signal is given in two forms, "wrong parse" flag, and text feedback "you cannot take that". These two signals are grouped together into a single binary signal which then provided to the algorithm. <br />
<br />
[[File:zork_domain.png|1200px|thumb|center|Left:the world of Zork.Right:subdomains of Zork.]]<br />
<br />
Experiments begin with the two subdomains of Zork domains: Egg Quest and the Troll Quest. For these subdomains, an additional reward signal is provided to guide the agent towards solving specific tasks and make the results more visible. A reward of -1 is applied at every time step to encourage the agent to favor short paths. Each trajectory terminates is upon completing the quest or after T steps are taken. The discounted factor for training is <math display="inline">\gamma=0.8</math> and <math display="inline">\gamma=1</math> during evaluation. Also <math display="inline">\beta=0.5, l=0.6</math> in all experiments. <br />
<br />
===Egg Quest===<br />
<br />
The goal for this quest is to find and open the jewel-encrusted egg hidden on a tree in the forest. An egg-splorer goes on an adventure to find a mystical ancient relic with his furry companion. You can have a look at the game at [https://scratch.mit.edu/projects/212838126/ EggQuest]<br />
<br />
The agent will get 100 points upon completing this task. For action space, there are 9 fixed actions for navigation, and a second subset which consisting <math display="inline">N_{Take}</math> actions for taking possible objects in the game. <math display="inline">N_{Take}=200 (set A_1), N_{Take}=300 (set A_2)</math> has been tested separately.<br />
AE-DQN (blue) and a vanilla DQN agent (green) has been tested in this quest.<br />
<br />
[[File:AEF_zork_comparison.png|1200px|thumb|center|Performance of agents in the egg quest.]]<br />
<br />
Figure a) corresponds to the set <math display="inline">A_1</math>, with T=100, b) corresponds to the set <math display="inline">A_2</math>, with T=100, and c) corresponds to the set <math display="inline">A_2</math>, with T=200. Both agents have performed well on sets a and c. However, the AE-DQN agent has learned much faster than the DQN on set b, which implies that action elimination is more robust to hyperparameter optimization when the action space is large. One important observation to note is that the three figures have different scales for the cumulative reward. While the AE-DQN outperformed the standard DQN in figure b, both models performed significantly better with the hyperparameter configuration in figure c.<br />
<br />
===Troll Quest===<br />
<br />
The goal of this quest is to find the troll. To do it the agent needs to find the way to the house, use a lantern to expose the hidden entrance to the underworld. It will get 100 points upon achieving the goal. This quest is a larger problem than Egg Quest. The action set <math display="inline">A_1</math> is 200 take actions and 15 necessary actions, 215 in total.<br />
<br />
[[File:AEF_troll_comparison.png|400px|thumb|center|Results in the Troll Quest.]]<br />
<br />
The red line above is an "optimal elimination" baseline which consists of only 35 actions(15 essential and 20 relevant take actions). We can see that AE-DQN still outperforms DQN and its improvement over DQN is more significant in the Troll Quest than the Egg quest. Also, it achieves compatible performance to the "optimal elimination" baseline.<br />
<br />
===Open Zork===<br />
<br />
Lastly, the "Open Zork" domain has been tested which only the environment reward has been used. 1M steps have been trained. Each trajectory terminates after T=200 steps. Two action sets have been used:<math display="inline">A_3</math>, the "Minimal Zork" action set, which is the minimal set of actions (131) that is required to solve the game. <math display="inline">A_4</math>, the "Open Zork" action set (1227) which composed of {Verb, Object} tuples for all the verbs and objects in the game.<br />
<br />
[[]]<br />
<br />
[[File:AEF_open_zork_comparison.png|600px|thumb|center|Results in "Open Zork".]]<br />
<br />
<br />
The above Figure shows the learning curve for both AE-DQN and DQN. We can see that AE-DQN (blue) still outperform the DQN (blue) in terms of speed and cumulative reward.<br />
<br />
=Conclusion=<br />
In this paper, the authors proposed a Deep Reinforcement Learning model for sub-optimal actions while performing Q-learning. Moreover, they showed that by eliminating actions, using linear contextual bandits with theoretical guarantees of convergence, the size of the action space is reduced, exploration is more effective, and learning is improved when tested on Zork, a text-based game.<br />
<br />
For future work the authors aim to investigate more sophisticated architectures and tackle learning shared representations for elimination and control which may boost performance on both tasks.<br />
<br />
They also hope to to investigate other mechanisms for action elimination, such as eliminating actions that result from low Q-values as in Even-Dar, Mannor, and Mansour, 2003.<br />
<br />
The authors also hope to generate elimination signals in real-world domains and achieve the purpose of eliminating the signal implicitly.<br />
<br />
=Critique=<br />
The paper is not a significant algorithmic contribution and it merely adds an extra layer of complexity to the very famous DQN algorithm. All the experimental domains considered in the paper are discrete action problems that have so many actions that it could have been easily extended to a continuous action problem. In continuous action space there are several policy gradient based RL algorithms that have provided stronger performances. The authors should have ideally compared their methods to such algorithms like PPO or DRPO.<br />
<br />
Even with the critique above, the paper presents mathematical/theoretical justifications of the methodology. Moreover, since the methodology is built on the standard RL framework, this means that other variant RL algorithms can apply the idea to decrease the complexity and increase the performance. Moreover, the there are some rooms for applying technical variations for the algorithm.<br />
<br />
Also, since we are utilizing the system's response to irrelevant actions, an intuitive approach to eliminate such irrelevant actions is to add a huge negative reward for such actions, which will be much easier than the approach suggested by this paper. However, the in experiments, the author only compares AE-DQN to traditional DQN, not traditional DQN with negative rewards assigned to irrelevant actions.<br />
<br />
After all, the name that the authors have chosen is a good and attractive choice and matches our brain's structure which in so many real-world scenarios detects what not to learn.<br />
<br />
=Reference=<br />
1. Chu, W.; Li, L.; Reyzin, L.; and Schapire, R. 2011. Contextual bandits with linear payoff functions. In Proceedings of the Fourteenth International Conference on Artiﬁcial Intelligence and Statistics.<br />
<br />
2. Côté,M.-A.;Kádár,Á.;Yuan,X.;Kybartas,B.;Barnes,T.;Fine,E.;Moore,J.;Hausknecht,M.;Asri, L. E.; Adada, M.; et al. 2018. Textworld: A learning environment for text-based games. arXiv.<br />
<br />
3. Dulac-Arnold, G.; Evans, R.; van Hasselt, H.; Sunehag, P.; Lillicrap, T.; Hunt, J.; Mann, T.; Weber, T.; Degris, T.; and Coppin, B. 2015. Deep reinforcement learning in large discrete action spaces. arXiv.<br />
<br />
4. He, J.; Chen, J.; He, X.; Gao, J.; Li, L.; Deng, L.; and Ostendorf, M. 2015. Deep reinforcement learning with an unbounded action space. CoRR abs/1511.04636.<br />
<br />
5. Kim, Y. 2014. Convolutional neural networks for sentence classiﬁcation. [https://arxiv.org/abs/1408.5882 arXiv preprint].<br />
<br />
6. VanHasselt,H.,andWiering,M.A. 2009. Usingcontinuousactionspacestosolvediscreteproblems. In Neural Networks, 2009. IJCNN 2009. International Joint Conference on, 1149–1156. IEEE.<br />
<br />
7. Watkins, C. J., and Dayan, P. 1992. Q-learning. Machine learning 8(3-4):279–292.<br />
<br />
8. Su, P.-H.; Gasic, M.; Mrksic, N.; Rojas-Barahona, L.; Ultes, S.; Vandyke, D.; Wen, T.-H.; and Young, S. 2016. Continuously learning neural dialogue management. arXiv preprint.<br />
<br />
9. Wu, Y.; Schuster, M.; Chen, Z.; Le, Q. V.; Norouzi, M.; Macherey, W.; Krikun, M.; Cao, Y.; Gao, Q.; Macherey, K.; et al. 2016. Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint.<br />
<br />
10. Yuan, X.; Côté, M.-A.; Sordoni, A.; Laroche, R.; Combes, R. T. d.; Hausknecht, M.; and Trischler, A. 2018. Counting to explore and generalize in text-based games. arXiv preprint arXiv:1806.1152<br />
<br />
11. Zahavy, T.; Haroush, M.; Merlis, N.; Mankowitz, D. J.; 2018. Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning. arXiv:1809.02121v1</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:zork_domain.png&diff=42085File:zork domain.png2018-11-30T17:05:40Z<p>Aghabuss: </p>
<hr />
<div></div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:Agent_performance.png&diff=42084File:Agent performance.png2018-11-30T17:03:09Z<p>Aghabuss: </p>
<hr />
<div></div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=learn_what_not_to_learn&diff=42083learn what not to learn2018-11-30T17:01:47Z<p>Aghabuss: </p>
<hr />
<div>=Introduction=<br />
<br />
In reinforcement learning, it is often difficult for an agent to learn when the action space is large, especially the difficulties from function approximation and exploration. In some cases many actions are irrelevant and it is sometimes easier for the algorithm to learn which action not to take. The paper proposes a new reinforcement learning approach for dealing with large action spaces based on action elimination by restricting the available actions in each state to a subset of the most likely ones. There is a core assumption being made in the proposed method that it is easier to predict which actions in each state are invalid or inferior and use that information for control. More specifically, it proposes a system that learns the approximation of a Q-function and concurrently learns to eliminate actions. The method utilizes an external elimination signal which incorporates domain-specific prior knowledge. For example, in parser-based text games, the parser gives feedback regarding irrelevant actions after the action is played (e.g., Player: "Climb the tree." Parser: "There are no trees to climb"). Then a machine learning model can be trained to generalize to unseen states. <br />
<br />
The paper focuses on tasks where both states and the actions are natural language. It introduces a novel deep reinforcement learning approach which has a Deep Q-Network (DQN) and an Action Elimination Network (AEN), both using the Convolutional Neural Networks (CNN) for Natural Language Processing (NLP) tasks. The AEN is trained to predict invalid actions, supervised by the elimination signal from the environment. The proposed method uses the final layer activations of AEN to build a linear contextual bandit model which allows the elimination of sub-optimal actions with high probability. '''Note that the core assumption is that it is easy to predict which actions are invalid or inferior in each state and leverage that information for control.'''<br />
<br />
The text-based game called "Zork", which lets players to interact with a virtual world through a text-based interface is tested by using the elimination framework. <br />
In this game, the player explores an environment using imagination of the text he/she reads. For more info, you can watch this video: [https://www.youtube.com/watch?v=xzUagi41Wo0 Zork].<br />
<br />
The AEN algorithm has achieved a faster learning rate than the baseline agents by eliminating irrelevant actions.<br />
<br />
Below shows an example for the Zork interface:<br />
<br />
[[File:lnottol_fig1.png|500px|center]]<br />
<br />
All states and actions are given in natural language. Input for the game contains more than a thousand possible actions in each state since the player can type anything.<br />
<br />
=Related Work=<br />
<br />
Text-Based Games(TBG): The state of the environment in TBG is described by simple language. The player interacts with the environment with text command which respects a pre-defined grammar. A popular example is Zork which has been tested in the paper. TBG is a good research intersection of RL and NLP, it requires language understanding, long-term memory, planning, exploration, affordability extraction, and common sense. It also often introduce stochastic dynamics to increase randomness.<br />
<br />
Representations for TBG: Good word representation is necessary in order to learn control policies from high-dimensional complex data such as text. Previous work on TBG used pre-trained embeddings directly for control, other works combined pre-trained embedding with neural networks. For example, He<br />
et al. (2015) proposed to consider an input as Bag Of Words features for a neural network, learned separately<br />
embeddings for states and actions, and then computed the Q function from autocorrelations between<br />
these embeddings.<br />
<br />
DRL with linear function approximation: DRL methods such as the DQN have achieved state-of-the-art results in a variety of challenging, high-dimensional domains. This is mainly because neural networks can learn rich domain representations for value function and policy. On the other hand, linear representation batch reinforcement learning methods are more stable and accurate, while feature engineering is necessary.<br />
<br />
RL in Large Action Spaces: Prior work concentrated on factorizing the action space into binary subspace(Pazis and Parr, 2011; Dulac-Arnold et al., 2012; Lagoudakis and Parr, 2003), other works proposed to embed the discrete actions into a continuous space, then choose the nearest discrete action according to the optimal actions in the continuous space(Dulac-Arnold et al., 2015; Van Hasselt and Wiering, 2009). He et. al. (2015)extended DQN to unbounded(natural language) action spaces.<br />
Learning to eliminate actions was first mentioned by (Even-Dar, Mannor, and Mansour, 2003). They proposed to learn confidence intervals around the value function in each state. Lipton et al.(2016a) proposed to learn a classifier that detects hazardous state and then use it to shape the reward. Fulda et al.(2017) presented a method for affordability extraction via inner products of pre-trained word embedding.<br />
<br />
=Action Elimination=<br />
<br />
The approach in the paper builds on the standard Reinforcement Learning formulation. At each time step <math>t</math>, the agent observes state <math display="inline">s_t </math> and chooses a discrete action <math display="inline">a_t\in\{1,...,|A|\} </math>. Then, after action execution, the agent obtains a reward <math display="inline">r_t(s_t,a_t) </math> and observes next state <math display="inline">s_{t+1} </math> according to a transition kernel <math>P(s_{t+1}|s_t,a_t)</math>. The goal of the algorithm is to learn a policy <math display="inline">\pi(a|s) </math> which maximizes the expected future discounted cumulative return <math display="inline">V^\pi(s)=E^\pi[\sum_{t=0}^{\infty}\gamma^tr(s_t,a_t)|s_0=s]</math>, where <math> 0< \gamma <1 </math>. The Q-function is <math display="inline">Q^\pi(s,a)=E^\pi[\sum_{t=0}^{\infty}\gamma^tr(s_t,a_t)|s_0=s,a_0=a]</math>, and it can be optimized by Q-learning algorithm.<br />
<br />
After executing an action, the agent observes a binary elimination signal <math>e(s, a)</math> to determine which actions not to take. It equals 1 if action <math>a</math> may be eliminated in state <math>s</math> (and 0 otherwise). The signal helps mitigating the problem of large discrete action spaces. We start with the following definitions:<br />
<br />
'''Definition 1:''' <br />
<br />
Valid state-action pairs with respect to an elimination signal are state action pairs which the elimination process should not eliminate. <br />
<br />
The set of valid state-action pairs contains all of the state-action pairs that are a part of some optimal policy, i.e., only strictly suboptimal state-actions can be invalid.<br />
<br />
'''Definition 2:'''<br />
<br />
Admissible state-action pairs with respect to an elimination algorithm are state action pairs which the elimination algorithm does not eliminate.<br />
<br />
'''Definition 3:'''<br />
<br />
Action Elimination Q-learning is a Q-learning algorithm which updates only admissible state-action pairs and chooses the best action in the next state from its admissible actions. We allow the base Q-learning algorithm to be any algorithm that converges to <math display="inline">Q^*</math> with probability 1 after observing each state-action infinitely often.<br />
<br />
==Advantages of Action Elimination==<br />
<br />
The main advantage of action elimination is that it allows the agent to overcome some of the main difficulties in large action spaces which are Function Approximation and Sample Complexity. <br />
<br />
Function approximation: Errors in the Q-function estimates may cause the learning algorithm to converge to a suboptimal policy, this phenomenon becomes more noticeable when the action space is large. Action elimination mitigates this effect by taking the max operator only on valid actions, thus, reducing potential overestimation errors. Besides, by ignoring the invalid actions, the function approximation can also learn a simpler mapping (i.e., only the Q-values of the valid state-action pairs) leading to faster convergence and better solution.<br />
<br />
Sample complexity: The sample complexity measures the number of steps during learning, in which the policy is not <math display="inline">\epsilon</math>-optimal. Assume that there are <math>A'</math> actions that should be eliminated and are <math>\epsilon</math>-optimal, i.e. their value is at least <math>V^*(s)-\epsilon</math>. The invalid action often returns no reward and doesn't change the state, (Lattimore and Hutter, 2012)resulting in an action gap of <math display="inline">\epsilon=(1-\gamma)V^*(s)</math>, and this translates to <math display="inline">V^*(s)^{-2}(1-\gamma)^{-5}log(1/\delta)</math> wasted samples for learning each invalid state-action pair. Practically, elimination algorithm can eliminate these invalid actions and therefore speed up the learning process approximately by <math display="inline">A/A'</math>.<br />
<br />
Because it is difficult to embed the elimination signal into the MDP, the authors use contextual multi-armed bandits to decouple the elimination signal from the MDP, which can correctly eliminate actions when applying standard Q learning into learning process.<br />
<br />
==Action elimination with contextual bandits==<br />
<br />
Contextual bandit problem is a famous probability problem and is a natural extension from the multi-arm bandit problem.<br />
<br />
Let <math display="inline">x(s_t)\in R^d </math> be the feature representation of <math display="inline">s_t </math>. We assume that under this representation there exists a set of parameters <math display="inline">\theta_a^*\in \mathbb{R}^d </math> such that the elimination signal in state <math display="inline">s_t </math> is <math display="inline">e_t(s_t,a) = \theta_a^{*T}x(s_t)+\eta_t </math>, where <math display="inline"> \Vert\theta_a^*\Vert_2\leq S</math>. <math display="inline">\eta_t</math> is an R-subgaussian random variable with zero mean that models additive noise to the elimination signal. When there is no noise in the elimination signal, R=0. Otherwise, <math display="inline">R\leq 1</math> since the elimination signal is bounded in [0,1]. Assume the elimination signal satisfies: <math display="inline">0\leq E[e_t(s_t,a)]\leq l </math> for any valid action and <math display="inline"> u\leq E[e_t(s_t, a)]\leq 1</math> for any invalid action. And <math display="inline"> l\leq u</math>. Denote by <math display="inline">X_{t,a}</math> as the matrix whose rows are the observed state representation vectors in which action a was chosen, up to time t. <math display="inline">E_{t,a}</math> as the vector whose elements are the observed state representation elimination signals in which action a was chosen, up to time t. Denote the solution to the regularized linear regression <math display="inline">\Vert X_{t,a}\theta_{t,a}-E_{t,a}\Vert_2^2+\lambda\Vert \theta_{t,a}\Vert_2^2 </math> (for some <math display="inline">\lambda>0</math>) by <math display="inline">\hat{\theta}_{t,a}=\bar{V}_{t,a}^{-1}X_{t,a}^TE_{t,a} </math>, where <math display="inline">\bar{V}_{t,a}=\lambda I + X_{t,a}^TX_{t,a}</math>.<br />
<br />
<br />
According to Theorem 2 in (Abbasi-Yadkori, Pal, and Szepesvari, 2011), <math display="inline">|\hat{\theta}_{t,a}^{T}x(s_t)-\theta_a^{*T}x(s_t)|\leq\sqrt{\beta_t(\delta)x(s_t)^T\bar{V}_{t,a}^{-1}x(s_t)}\ \forall t>0</math>, where <math display="inline">\sqrt{\beta_t(\delta)}=R\sqrt{2\ \text{log}(\text{det}(\bar{V}_{t,a})^{1/2}\text{det}(\lambda I)^{-1/2}/\delta)}+\lambda^{1/2}S</math>, with probability of at least <math display="inline">1-\delta</math>. If <math display="inline">\forall s\ ,\Vert x(s)\Vert_2 \leq L</math>, then <math display="inline">\beta_t</math> can be bounded by <math display="inline">\sqrt{\beta_t(\delta)} \leq R \sqrt{d\ \text{log}(1+tL^2/\lambda/\delta)}+\lambda^{1/2}S</math>. Next, define <math display="inline">\tilde{\delta}=\delta/k</math> and bound this probability for all the actions. i.e., <math display="inline">\forall a,t>0</math><br />
<br />
<math display="inline">Pr(|\hat{\theta}_{t-1,a}^{T}x(s_t)-\theta_{t-1, a}^{*T}x(s_t)|\leq\sqrt{\beta_t(\tilde\delta)x(s_t)^T\bar{V}_{t - 1,a}^{-1}x(s_t)}) \leq 1-\delta</math><br />
<br />
Recall that <math display="inline">E[e_t(s,a)]=\theta_a^{*T}x(s_t)\leq l</math> if a is a valid action. Then we can eliminate action a at state <math display="inline">s_t</math> if it satisfies:<br />
<br />
<math display="inline">\hat{\theta}_{t-1,a}^{T}x(s_t)-\sqrt{\beta_{t-1}(\tilde\delta)x(s_t)^T\bar{V}_{t-1,a}^{-1}x(s_t)})>l</math><br />
<br />
with probability <math display="inline">1-\delta</math> that we never eliminate any valid action. Note that <math display="inline">l, u</math> are not known. In practice, choosing <math display="inline">l</math> to be 0.5 should suffice.<br />
<br />
==Concurrent Learning==<br />
In fact, Q-learning and contextual bandit algorithms can learn simultaneously, resulting in the convergence of both algorithms, i.e., finding an optimal policy and a minimal valid action space. <br />
<br />
If the elimination is done based on the concentration bounds of the linear contextual bandits, it can be ensured that Action Elimination Q-learning converges, as shown in Proposition 1.<br />
<br />
'''Proposition 1:'''<br />
<br />
Assume that all state action pairs (s,a) are visited infinitely often, unless eliminated according to <math display="inline">\hat{\theta}_{t-1,a}^Tx(s)-\sqrt{\beta_{t-1}(\tilde{\delta})x(s)^T\bar{V}_{t-1,a}^{-1}x(s))}>l</math>. Then, with a probability of at least <math display="inline">1-\delta</math>, action elimination Q-learning converges to the optimal Q-function for any valid state-action pairs. In addition, actions which should be eliminated are visited at most <math display="inline">T_{s,a}(t)\leq 4\beta_t/(u-l)^2<br />
+1</math> times.<br />
<br />
Notice that when there is no noise in the elimination signal(R=0), we correctly eliminate actions with probability 1. so invalid actions will be sampled a finite number of times.<br />
<br />
=Method=<br />
<br />
The assumption that <math display="inline">e_t(s_t,a)=\theta_a^{*T}x(s_t)+\eta_t </math> generally does not hold when using raw features like word2vec. So the paper proposes to use the neural network's last layer as feature representation of states. A practical challenge here is that the features must be fixed over time when used by the contextual bandit. So batch-updates framework(Levine et al., 2017;Riquelme, Tucker, and Snoek, 2018) is used, where a new contextual bandit model is learned for every few steps that uses the last layer activation of the AEN as features.<br />
<br />
==Architecture of action elimination framework==<br />
<br />
[[File:lnottol_fig1b.png|300px|center]]<br />
<br />
After taking action <math display="inline">a_t</math>, the agent observes <math display="inline">(r_t,s_{t+1},e_t)</math>. The agent uses it to learn two function approximation deep neural networks: A DQN and an AEN. AEN provides an admissible actions set <math display="inline">A'</math> to the DQN, which uses this set to decide how to act and learn. The architecture for both the AEN and DQN is an NLP CNN(100 convolutional filters for AEN and 500 for DQN, with three different 1D kernels of length (1,2,3)), based on(Kim, 2014). The state is represented as a sequence of words, composed of the game descriptor and the player's inventory. These are truncated or zero padded to a length of 50 descriptor + 15 inventory words and each word is embedded into continuous vectors using word2vec in <math display="inline">R^{300}</math>. The features of the last four states are then concatenated together such that the final state representations s are in <math display="inline">R^{78000}</math>. The AEN is trained to minimize the MSE loss, using the elimination signal as a label. The code, the Zork domain, and the implementation of the elimination signal can be found [https://github.com/TomZahavy/CB_AE_DQN here.]<br />
<br />
==Psuedocode of the Algorithm==<br />
<br />
[[File:lnottol_fig2.png|750px|center]]<br />
<br />
AE-DQN trains two networks: a DQN denoted by Q and an AEN denoted by E. The algorithm creates a linear contextual bandit model from it every L iterations with procedure AENUpdate(). This procedure uses the activations of the last hidden layer of E as features, which are then used to create a contextual linear bandit model.AENUpdate() then solved this model and plugin it into the target AEN. The contextual linear bandit model <math display="inline">(E^-,V)</math> is then used to eliminate actions via the ACT() and Target() functions. ACT() follows an <math display="inline">\epsilon</math>-greedy mechanism on the admissible actions set. For exploitation, it selects the action with highest Q-value by taking an argmax on Q-values among <math display="inline">A'</math>. For exploration, it selects an action uniformly from <math display="inline">A'</math>. The targets() procedure is estimating the value function by taking max over Q-values only among admissible actions, hence, reducing function approximation errors.<br />
<br />
=Experiments=<br />
==Grid Domain==<br />
The authors start by evaluating our algorithm on a small grid world domain with 9 rooms, where they ca analyze the effect of the action elimination (visualization can be found in the appendix). In this domain, the agent starts at the center of the grid and needs to navigate to its upper-left corner. On every step, the agent suffers a penalty of (−1), with a terminal reward of 0. Prior to the game, the states are randomly divided into K categories. The environment has 4K navigation actions, 4 for each category, each with a probability to move in a random direction. If the chosen action belongs to the same category as the state, the action is performed correctly in probability pTc = 0.75. Otherwise, it will be performed correctly in probability pFc = 0.5. If the action does not fit the state category, the elimination signal equals 1, and if the action and state belong to the same category, then e = 0. The optimal policy will only use the navigation actions from the same type as the state, and all of the other actions are strictly suboptimal. A basic comparison between vanilla Q-learning without action elimination (green) and a tabular version of the action elimination Q-learning (blue) can be found in the figure below. In all of the figures, the results are compared to the case with one category (red), i.e., only 4 basic navigation actions, which forms an upper bound on performance with multiple categories. In Figure (a),(c), the episode length is T = 150, and in Figure (b) it is T = 300, to allow sufficient exploration for the vanilla Q-Learning. It is clear from the simulations that the action elimination dramatically improves the results in large action spaces. Also, note that the gain from action elimination increases with the grid size since the elimination allows the agent to reach the goal earlier.<br />
<br />
<br />
[[File:griddomain.png|1200px|thumb|center|Performance of agents in grid world]]<br />
==Zork domain==<br />
<br />
The world of Zork presents a rich environment with a large state and action space. <br />
Zork players describe their actions using natural language instructions. For example, "open the mailbox". Then their actions were processed by a sophisticated natural language parser. Based on the results, the game presents the outcome of the action. The goal of Zork is to collect the Twenty Treasures of Zork and install them in the trophy case. Points that are generated from the game's scoring system are given to the agent as the reward. For example, the player gets the points when solving the puzzles. Placing all treasures in the trophy will get 350 points. The elimination signal is given in two forms, "wrong parse" flag, and text feedback "you cannot take that". These two signals are grouped together into a single binary signal which then provided to the algorithm. <br />
<br />
Experiments begin with the two subdomains of Zork domains: Egg Quest and the Troll Quest. For these subdomains, an additional reward signal is provided to guide the agent towards solving specific tasks and make the results more visible. A reward of -1 is applied at every time step to encourage the agent to favor short paths. Each trajectory terminates is upon completing the quest or after T steps are taken. The discounted factor for training is <math display="inline">\gamma=0.8</math> and <math display="inline">\gamma=1</math> during evaluation. Also <math display="inline">\beta=0.5, l=0.6</math> in all experiments. <br />
<br />
===Egg Quest===<br />
<br />
The goal for this quest is to find and open the jewel-encrusted egg hidden on a tree in the forest. An egg-splorer goes on an adventure to find a mystical ancient relic with his furry companion. You can have a look at the game at [https://scratch.mit.edu/projects/212838126/ EggQuest]<br />
<br />
The agent will get 100 points upon completing this task. For action space, there are 9 fixed actions for navigation, and a second subset which consisting <math display="inline">N_{Take}</math> actions for taking possible objects in the game. <math display="inline">N_{Take}=200 (set A_1), N_{Take}=300 (set A_2)</math> has been tested separately.<br />
AE-DQN (blue) and a vanilla DQN agent (green) has been tested in this quest.<br />
<br />
[[File:AEF_zork_comparison.png|1200px|thumb|center|Performance of agents in the egg quest.]]<br />
<br />
Figure a) corresponds to the set <math display="inline">A_1</math>, with T=100, b) corresponds to the set <math display="inline">A_2</math>, with T=100, and c) corresponds to the set <math display="inline">A_2</math>, with T=200. Both agents have performed well on sets a and c. However, the AE-DQN agent has learned much faster than the DQN on set b, which implies that action elimination is more robust to hyperparameter optimization when the action space is large. One important observation to note is that the three figures have different scales for the cumulative reward. While the AE-DQN outperformed the standard DQN in figure b, both models performed significantly better with the hyperparameter configuration in figure c.<br />
<br />
===Troll Quest===<br />
<br />
The goal of this quest is to find the troll. To do it the agent needs to find the way to the house, use a lantern to expose the hidden entrance to the underworld. It will get 100 points upon achieving the goal. This quest is a larger problem than Egg Quest. The action set <math display="inline">A_1</math> is 200 take actions and 15 necessary actions, 215 in total.<br />
<br />
[[File:AEF_troll_comparison.png|400px|thumb|center|Results in the Troll Quest.]]<br />
<br />
The red line above is an "optimal elimination" baseline which consists of only 35 actions(15 essential and 20 relevant take actions). We can see that AE-DQN still outperforms DQN and its improvement over DQN is more significant in the Troll Quest than the Egg quest. Also, it achieves compatible performance to the "optimal elimination" baseline.<br />
<br />
===Open Zork===<br />
<br />
Lastly, the "Open Zork" domain has been tested which only the environment reward has been used. 1M steps have been trained. Each trajectory terminates after T=200 steps. Two action sets have been used:<math display="inline">A_3</math>, the "Minimal Zork" action set, which is the minimal set of actions (131) that is required to solve the game. <math display="inline">A_4</math>, the "Open Zork" action set (1227) which composed of {Verb, Object} tuples for all the verbs and objects in the game.<br />
<br />
[[]]<br />
<br />
[[File:AEF_open_zork_comparison.png|600px|thumb|center|Results in "Open Zork".]]<br />
<br />
<br />
The above Figure shows the learning curve for both AE-DQN and DQN. We can see that AE-DQN (blue) still outperform the DQN (blue) in terms of speed and cumulative reward.<br />
<br />
=Conclusion=<br />
In this paper, the authors proposed a Deep Reinforcement Learning model for sub-optimal actions while performing Q-learning. Moreover, they showed that by eliminating actions, using linear contextual bandits with theoretical guarantees of convergence, the size of the action space is reduced, exploration is more effective, and learning is improved when tested on Zork, a text-based game.<br />
<br />
For future work the authors aim to investigate more sophisticated architectures and tackle learning shared representations for elimination and control which may boost performance on both tasks.<br />
<br />
They also hope to to investigate other mechanisms for action elimination, such as eliminating actions that result from low Q-values as in Even-Dar, Mannor, and Mansour, 2003.<br />
<br />
The authors also hope to generate elimination signals in real-world domains and achieve the purpose of eliminating the signal implicitly.<br />
<br />
=Critique=<br />
The paper is not a significant algorithmic contribution and it merely adds an extra layer of complexity to the very famous DQN algorithm. All the experimental domains considered in the paper are discrete action problems that have so many actions that it could have been easily extended to a continuous action problem. In continuous action space there are several policy gradient based RL algorithms that have provided stronger performances. The authors should have ideally compared their methods to such algorithms like PPO or DRPO.<br />
<br />
Even with the critique above, the paper presents mathematical/theoretical justifications of the methodology. Moreover, since the methodology is built on the standard RL framework, this means that other variant RL algorithms can apply the idea to decrease the complexity and increase the performance. Moreover, the there are some rooms for applying technical variations for the algorithm.<br />
<br />
Also, since we are utilizing the system's response to irrelevant actions, an intuitive approach to eliminate such irrelevant actions is to add a huge negative reward for such actions, which will be much easier than the approach suggested by this paper. However, the in experiments, the author only compares AE-DQN to traditional DQN, not traditional DQN with negative rewards assigned to irrelevant actions.<br />
<br />
After all, the name that the authors have chosen is a good and attractive choice and matches our brain's structure which in so many real-world scenarios detects what not to learn.<br />
<br />
=Reference=<br />
1. Chu, W.; Li, L.; Reyzin, L.; and Schapire, R. 2011. Contextual bandits with linear payoff functions. In Proceedings of the Fourteenth International Conference on Artiﬁcial Intelligence and Statistics.<br />
<br />
2. Côté,M.-A.;Kádár,Á.;Yuan,X.;Kybartas,B.;Barnes,T.;Fine,E.;Moore,J.;Hausknecht,M.;Asri, L. E.; Adada, M.; et al. 2018. Textworld: A learning environment for text-based games. arXiv.<br />
<br />
3. Dulac-Arnold, G.; Evans, R.; van Hasselt, H.; Sunehag, P.; Lillicrap, T.; Hunt, J.; Mann, T.; Weber, T.; Degris, T.; and Coppin, B. 2015. Deep reinforcement learning in large discrete action spaces. arXiv.<br />
<br />
4. He, J.; Chen, J.; He, X.; Gao, J.; Li, L.; Deng, L.; and Ostendorf, M. 2015. Deep reinforcement learning with an unbounded action space. CoRR abs/1511.04636.<br />
<br />
5. Kim, Y. 2014. Convolutional neural networks for sentence classiﬁcation. [https://arxiv.org/abs/1408.5882 arXiv preprint].<br />
<br />
6. VanHasselt,H.,andWiering,M.A. 2009. Usingcontinuousactionspacestosolvediscreteproblems. In Neural Networks, 2009. IJCNN 2009. International Joint Conference on, 1149–1156. IEEE.<br />
<br />
7. Watkins, C. J., and Dayan, P. 1992. Q-learning. Machine learning 8(3-4):279–292.<br />
<br />
8. Su, P.-H.; Gasic, M.; Mrksic, N.; Rojas-Barahona, L.; Ultes, S.; Vandyke, D.; Wen, T.-H.; and Young, S. 2016. Continuously learning neural dialogue management. arXiv preprint.<br />
<br />
9. Wu, Y.; Schuster, M.; Chen, Z.; Le, Q. V.; Norouzi, M.; Macherey, W.; Krikun, M.; Cao, Y.; Gao, Q.; Macherey, K.; et al. 2016. Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint.<br />
<br />
10. Yuan, X.; Côté, M.-A.; Sordoni, A.; Laroche, R.; Combes, R. T. d.; Hausknecht, M.; and Trischler, A. 2018. Counting to explore and generalize in text-based games. arXiv preprint arXiv:1806.1152<br />
<br />
11. Zahavy, T.; Haroush, M.; Merlis, N.; Mankowitz, D. J.; 2018. Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning. arXiv:1809.02121v1</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=ShakeDrop_Regularization&diff=41873ShakeDrop Regularization2018-11-29T17:07:30Z<p>Aghabuss: </p>
<hr />
<div>=Introduction=<br />
Current state of the art techniques for object classification are deep neural networks based on the residual block, first published by (He et al., 2016). This technique has been the foundation of several improved networks, including Wide ResNet (Zagoruyko & Komodakis, 2016), PyramdNet (Han et al., 2017) and ResNeXt (Xie et al., 2017). They have been further improved by regularization, such as Stochastic Depth (ResDrop) (Huang et al., 2016) and Shake-Shake (Gastaldi, 2017), which can avoid some problem like vanishing gradients. Shake-Shake applied to ResNeXt has achieved one of the lowest error rates on the CIFAR-10 and CIFAR-100 datasets. However, it is only applicable to multi-branch architectures and is not memory efficient since it requires two branches of residual blocks to apply. To address this problem, ShakeDrop regularization that can realize a similar disturbance to Shake-Shake on a single residual block is proposed. Moreover, they use ResDrop to stabilize the learning process. This paper seeks to formulate a general expansion of Shake-Shake that can be applied to any residual block based network.<br />
<br />
=Existing Methods=<br />
<br />
'''Deep Approaches'''<br />
<br />
'''ResNet''', was the first use of residual blocks, a foundational feature in many modern state of the art convolution neural networks. They can be formulated as <math>G(x) = x + F(x)</math> where <math>x</math> and <math>G(x)</math> are the input and output of the residual block, and <math>F(x)</math> is the output of the residual branch on the residual block. A residual block typically performs a convolution operation and then passes the result plus its input onto the next block.<br />
<br />
Intuition behind Residual blocks:<br />
If the identity mapping is optimal, We can easily push the residuals to zero (F(x) = 0) than to fit an identity mapping (x, input=output) by a stack of non-linear layers. In simple language it is very easy to come up with a solution like F(x) =0 rather than F(x)=x using stack of non-linear cnn layers as function (Think about it). So, this function F(x) is what the authors called Residual function ([https://medium.com/@14prakash/understanding-and-implementing-architectures-of-resnet-and-resnext-for-state-of-the-art-image-cf51669e1624 Reference]).<br />
<br />
<br />
[[File:ResidualBlock.png|580px|centre|thumb|An example of a simple residual block from Deep Residual Learning for Image Recognition by He et al., 2016]]<br />
<br />
ResNet is constructed out of a large number of these residual blocks sequentially stacked. It is interesting to note that having too many layers can cause overfitting, as pointed out by He et al. (2016) with the high error rates for the 1,202-layer ResNet on CIFAR datasets. Another paper (Veit et al., 2016) empirically showed that the cause of the high error rates can be mostly attributed to specific residual blocks whose channels increase greatly.<br />
<br />
'''PyramidNet''' is an important iteration that built on ResNet and WideResNet by gradually increasing channels on each residual block. The residual block is similar to those used in ResNet. It has been used to generate some of the first successful convolution neural networks with very large depth, at 272 layers. Amongst unmodified residual network architectures, it performs the best on the CIFAR datasets.<br />
<br />
[[File:ResidualBlockComparison.png|980px|centre|thumb|A simple illustration of different residual blocks from Deep Pyramidal Residual Networks by Han et al., 2017. The width of a block reflects the number of channels used in that layer.]]<br />
<br />
<br />
'''Non-Deep Approaches'''<br />
<br />
'''Wide ResNet''' modified ResNet by increasing channels in each layer, having a wider and shallower structure. Similarly to PyramidNet, this architecture avoids some of the pitfalls in the original formulation of ResNet.<br />
<br />
'''ResNeXt''' achieved performance beyond that of Wide ResNet with only a small increase in the number of parameters. It can be formulated as <math>G(x) = x + F_1(x)+F_2(x)</math>. In this case, <math>F_1(x)</math> and <math>F_2(x)</math> are the outputs of two paired convolution operations in a single residual block. The number of branches is not limited to 2, and will control the result of this network.<br />
<br />
<br />
[[File:SimplifiedResNeXt.png|600px|centre|thumb|Simplified ResNeXt Convolution Block. Yamada et al., 2018]]<br />
<br />
<br />
'''Regularization Methods'''<br />
<br />
'''Stochastic Depth''' helped address the issue of vanishing gradients in ResNet. It works by randomly dropping residual blocks. On the <math>l^{th}</math> residual block the Stochastic Depth process is given as <math>G(x)=x+b_lF(x)</math> where <math>b_l \in \{0,1\}</math> is a Bernoulli random variable with probability <math>p_l</math>. Using a constant value for <math>p_l</math> didn't work well, so instead a linear decay rule <math>p_l = 1 - \frac{l}{L}(1-p_L)</math> was used. In this equation, <math>L</math> is the number of layers, and <math>p_L</math> is the initial parameter. <br />
<br />
'''Shake-Shake''' is a regularization method that specifically improves the ResNeXt architecture. It can be given as <math>G(x)=x+\alpha F_1(x)+(1-\alpha)F_2(x)</math>, where <math>\alpha \in [0,1]</math> is a random coefficient. <math>\alpha</math> is used during the forward pass, and another identically distributed random parameter <math>\beta</math> is used in the backward pass. This caused one of the two paired convolution operations to be dropped, and further improved ResNeXt.<br />
<br />
[[File:Paper 32.jpg|600px|centre|thumb| Shake-Shake (ResNeXt + Shake-Shake) (Gastaldi, 2017), in which some processing layers omitted for conciseness.]]<br />
<br />
=Proposed Method=<br />
This paper seeks to generalize the method proposed in Shake-Shake to be applied to any residual structure network. Shake-Shake. The initial formulation of 1-branch shake is <math>G(x) = x + \alpha F(x)</math>. In this case, <math>\alpha</math> is a coefficient that disturbs the forward pass, but is not necessarily constrained to be [0,1]. Another corresponding coefficient <math>\beta</math> is used in the backwards pass. Applying this simple adaptation of Shake-Shake on a 110-layer version of PyramidNet with <math>\alpha \in [0,1]</math> and <math>\beta \in [0,1]</math> performs abysmally, with an error rate of 77.99%.<br />
<br />
This failure is a result of the setup causing too much perturbation. A trick is needed to promote learning with large perturbations, to preserve the regularization effect. The idea of the authors is to borrow from ResDrop and combine that with Shake-Shake. This works by randomly deciding whether to apply 1-branch shake. This creates in effect two networks, the original network without a regularization component, and a regularized network. When mixing up two networks, we expected the following effects: When the non regularized network is selected, learning is promoted; when the perturbed network is selected, learning is disturbed. Achieving good performance requires a balance between the two. <br />
<br />
'''ShakeDrop''' is given as <br />
<br />
<div align="center"><br />
<math>G(x) = x + (b_l + \alpha - b_l \alpha)F(x)</math>,<br />
</div><br />
<br />
where <math>b_l</math> is a Bernoulli random variable following the linear decay rule used in Stochastic Depth. An alternative presentation is <br />
<br />
<div align="center"><br />
<math><br />
G(x) = \begin{cases}<br />
x + F(x) ~~ \text{if } b_l = 1 \\<br />
x + \alpha F(x) ~~ \text{otherwise}<br />
\end{cases}<br />
</math><br />
</div><br />
<br />
If <math>b_l = 1</math> then ShakeDrop is equivalent to the original network, otherwise it is the network + 1-branch Shake. The authors also found that the linear decay rule of ResDrop works well, compared with the uniform rule. Regardless of the value of <math>\beta</math> on the backwards pass, network weights will be updated.<br />
<br />
=Experiments=<br />
<br />
'''Parameter Search'''<br />
<br />
The authors experiments began with a hyperparameter search utilizing ShakeDrop on pyramidal networks. The PyramidNet used was made up of a total of 110 layers which included a convolutional layer and a final fully connected layer. It had 54 additive pyramidal residual blocks and the final residual block had 286 channels. The results of this search are presented below. <br />
<br />
[[File:ShakeDropHyperParameterSearch.png|600px|centre|thumb|Average Top-1 errors (%) of “PyramidNet + ShakeDrop” with several ranges of parameters of 4 runs at the final (300th) epoch on CIFAR-100 dataset in the “Batch” level. In some settings, it is equivalent to PyramidNet and PyramidDrop. Borrowed from ShakeDrop Regularization by Yamada et al., 2018.]]<br />
<br />
The setting that are used throughout the rest of the experiments are then <math>\alpha \in [-1,1]</math> and <math>\beta \in [0,1]</math>. Cases H and F outperform PyramidNet, suggesting that the strong perturbations imposed by ShakeDrop are functioning as intended. However, fully applying the perturbations in the backwards pass appears to destabilize the network, resulting in performance that is worse than standard PyramidNet.<br />
<br />
[[File:ParameterUpdateShakeDrop.png|400px|centre]]<br />
<br />
Following this initial parameter decision, the authors tested 4 different strategies for parameter update among "Batch" (same coefficients for all images in minibatch for each residual block), "Image" (same scaling coefficients for each image for each residual block), "Channel" (same scaling coefficients for each element for each residual block), and "Pixel" (same scaling coefficients for each element for each residual block). While Pixel was the best in terms of error rate, it is not very memory efficient, so Image was selected as it had the second best performance without the memory drawback.<br />
<br />
'''Comparison with Regularization Methods'''<br />
<br />
For these experiments, there are a few modifications that were made to assist with training. For ResNeXt, the EraseRelu formulation has each residual block ends in batch normalization. The Wide ResNet also is compared between vanilla with batch normalization and without. Batch normalization keeps the outputs of residual blocks in a certain range, as otherwise <math>\alpha</math> and <math>\beta</math> could cause perturbations that are too large, causing divergent learning. There is also a comparison of ResDrop/ShakeDrop Type A (where the regularization unit is inserted before the add unit for a residual branch) and after (where the regularization unit is inserted after the add unit for a residual branch). <br />
<br />
These experiments are performed on the CIFAR-100 dataset.<br />
<br />
[[File:ShakeDropArchitectureComparison1.png|800px|centre|thumb|]]<br />
<br />
[[File:ShakeDropArchitectureComparison2.png|800px|centre|thumb|]]<br />
<br />
[[File:ShakeDropArchitectureComparison3.png|800px|centre|thumb|]]<br />
<br />
For a final round of testing, the training setup was modified to incorporate other techniques used in state of the art methods. For most of the tests, the learning rate for the 300 epoch version started at 0.1 and decayed by a factor of 0.1 1/2 & 3/4 of the way through training. The alternative was cosine annealing, based on the presentation by Loshchilov and Hutter in their paper SGDR: Stochastic Gradient Descent with Warm Restarts. This is indicated in the Cos column, with a check indicating cosine annealing. <br />
<br />
[[File:CosineAnnealing.png|400px|centre|thumb|]]<br />
<br />
The Reg column indicates the regularization method used, either none, ResDrop (RD), Shake-Shake (SS), or ShakeDrop (SD). Fianlly, the Fil Column determines the type of data augmentation used, either none, cutout (CO) (DeVries & Taylor, 2017b), or Random Erasing (RE) (Zhong et al., 2017). <br />
<br />
[[File:ShakeDropComparison.png|800px|centre|thumb|Top-1 Errors (%) at final epoch on CIFAR-10/100 datasets]]<br />
<br />
'''State-of-the-Art Comparisons'''<br />
<br />
A direct comparison with state of the art methods is favorable for this new method. <br />
<br />
# Fair comparison of ResNeXt + Shake-Shake with PyramidNet + ShakeDrop gives an improvement of 0.19% on CIFAR-10 and 1.86% on CIFAR-100. Under these conditions, the final error rate is then 2.67% for CIFAR-10 and 13.99% for CIFAR-100.<br />
# Fair comparison of ResNeXt + Shake-Shake + Cutout with PyramidNet + ShakeDrop + Random Erasing gives an improvement of 0.25% on CIFAR-10 and 3.01% on CIFAR 100. Under these conditions, the final error rate is then 2.31% for CIFAR-10 and 12.19% for CIFAR-100.<br />
# Comparison with the state-of-the-arts, PyramidNet + ShakeDrop gives an improvement of 0.25% on CIFAR-10 than ResNeXt + Shake-Shake + Cutout, PyramidNet + ShakeDrop gives an improvement of 2.85% on CIFAR-100 than Coupled Ensemble.<br />
<br />
=Implementation details=<br />
<br />
'''CIFAR-10/100 datasets'''<br />
<br />
All the images in these datasets were color normalized and then horizontally flipped with a probability of 50%. All of then then were zero padded to have a dimentionality of 40 by 40 pixels.<br />
<br />
<br />
=Conclusion=<br />
<br />
This paper proposed a new stochastic regularization method, ShakeDrop, which outperforms previous state of the art methods while maintaining similar memory efficiency. It demonstrates that heavily perturbing a network can help to overcome issues with overfitting. It is also an effective way to regularize residual networks for image classification. The method was tested by CIFAR-10/100 and Tiny ImageNet datasets and showed great performance.<br />
<br />
=Critique=<br />
<br />
The novelty of this paper is low as pointed out by the reviewers. The proposed ShakeDrop regularization is essentially a combination of the PyramidDrop and Shake-Shake regularization. The most surprising part is that the forward weight can be negative thus inverting the output of a convolution. The mathematical justification for ShakeDrop regularization is limited, relying on intuition and empirical evidence instead.<br />
As pointed out from the above, the method basically relies heavily on the intuition. This means that the performance of the algorithm can vary a lot depending on the characteristics of data sets that users are performing, with some exaggeration. However, the performance is still impressive since it performs better than known algorithms.<br />
<br />
=References=<br />
[Yamada et al., 2018] Yamada Y, Iwamura M, Kise K. ShakeDrop regularization. arXiv preprint arXiv:1802.02375. 2018 Feb 7.<br />
<br />
[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proc. CVPR, 2016.<br />
<br />
[Zagoruyko & Komodakis, 2016] Sergey Zagoruyko and Nikos Komodakis. Wide residual networks. In Proc. BMVC, 2016.<br />
<br />
[Han et al., 2017] Dongyoon Han, Jiwhan Kim, and Junmo Kim. Deep pyramidal residual networks. In Proc. CVPR, 2017a.<br />
<br />
[Xie et al., 2017] Saining Xie, Ross Girshick, Piotr Dollar, Zhuowen Tu, and Kaiming He. Aggregated residual transformations for deep neural networks. In Proc. CVPR, 2017.<br />
<br />
[Huang et al., 2016] Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Weinberger. Deep networks with stochastic depth. arXiv preprint arXiv:1603.09382v3, 2016.<br />
<br />
[Gastaldi, 2017] Xavier Gastaldi. Shake-shake regularization. arXiv preprint arXiv:1705.07485v2, 2017.<br />
<br />
[Loshilov & Hutter, 2016] Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016.<br />
<br />
[DeVries & Taylor, 2017b] Terrance DeVries and Graham W. Taylor. Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552, 2017b.<br />
<br />
[Zhong et al., 2017] Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang. Random erasing data augmentation. arXiv preprint arXiv:1708.04896, 2017.<br />
<br />
[Dutt et al., 2017] Anuvabh Dutt, Denis Pellerin, and Georges Qunot. Coupled ensembles of neural networks. arXiv preprint 1709.06053v1, 2017.<br />
<br />
[Veit et al., 2016] Andreas Veit, Michael J Wilber, and Serge Belongie. Residual networks behave like ensembles of relatively shallow networks. Advances in Neural Information Processing Systems 29, 2016.</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=CapsuleNets&diff=41845CapsuleNets2018-11-29T16:41:37Z<p>Aghabuss: /* References */</p>
<hr />
<div>The paper "Dynamic Routing Between Capsules" was written by three researchers at Google Brain: Sara Sabour, Nicholas Frosst, and Geoffrey E. Hinton. This paper was published and presented at the 31st Conference on Neural Information Processing Systems (NIPS 2017) in Long Beach, California. The same three researchers recently published a highly related paper "Matrix Capsules with EM Routing" for ICLR 2018.<br />
<br />
=Motivation=<br />
<br />
Ever since AlexNet eclipsed the performance of competing architectures in the 2012 ImageNet challenge, convolutional neural networks have maintained their dominance in computer vision applications. Despite the recent successes and innovations brought about by convolutional neural networks, some assumptions made in these networks are perhaps unwarranted and deficient. Using a novel neural network architecture, the authors create CapsuleNets, a network that they claim is able to learn image representations in a more robust, human-like manner. With only a 3 layer capsule network, they achieved near state-of-the-art results on MNIST.<br />
==Adversarial Examples==<br />
<br />
First discussed by Christian Szegedy et. al. in late 2013, adversarial examples have been heavily discussed by the deep learning community as a potential security threat to AI learning. Adversarial examples are defined as inputs that an attacker creates intentionally fool a machine learning model. An example of an adversarial example is shown below: <br />
<br />
[[File:adversarial_img_1.png |center]]<br />
To the human eye, the image appears to be a panda both before and after noise is injected into the image, whereas the trained ConvNet model discerns the noisy image as a Gibbon with almost 100% certainty. The fact that the network is unable to classify the above image as a panda after the epsilon perturbation leads to many potential security risks in AI dependent systems such as self-driving vehicles. Although various methods have been suggested to combat adversarial examples, robust defences are hard to construct due to the inherent difficulties in constructing theoretical models for the adversarial example crafting process. However, beyond the fact that these examples may serve as a security threat, it emphasizes that these convolutional neural networks do not learn image classification/object detection patterns the same way that a human would. Rather than identifying the core features of a panda such as: its eyes, mouth, nose, and the gradient changes in its black/white fur, the convolutional neural network seems to be learning image representations in a completely different manner. Deep learning researchers often attempt to model neural networks after human learning, and it is clear that further steps must be taken to robustify ConvNets against targeted noise perturbations.<br />
<br />
==Drawbacks of CNNs==<br />
Hinton claims that the key fault with traditional CNNs lies within the pooling function. Although pooling builds translational invariance into the network, it fails to preserve spatial relationships between objects. When we pool, we effectively reduce a kxk kernel of convolved cells into a scalar input. This results in a desired local invariance without inhibiting the network's ability to detect features, but causes valuable spatial information to be lost.<br />
<br />
In the example below, the network is able to detect the similar features (eyes, mouth, nose, etc) within both images, but fails to recognize that one image is a human face, while the other is a Picasso-esque due to the CNN's inability to encode spatial relationships after multiple pooling layers.<br />
<br />
<br />
[[File:Equivariance Face.png |center]]<br />
<br />
Conversely, we hope that a CNN can recognize that both of the following pictures contain a kitten. Unfortunately, when we feed the two images into a ResNet50 architecture, only the first image is correctly classified, while the second image is predicted to be a guinea pig.<br />
<br />
<br />
[[File:kitten.jpeg |center]]<br />
<br />
<br />
[[File:kitten-rotated-180.jpg |center]]<br />
<br />
For a more in depth discussion on the problems with ConvNets, please listen to Geoffrey Hinton's talk "What is wrong with convolutional neural nets?" given at MIT during the Brain & Cognitive Sciences - Fall Colloquium Series (December 4, 2014).<br />
<br />
==Intuition for Capsules==<br />
Human vision ignores irrelevant details by using a carefully determined sequence of fixation points to ensure that only a tiny fraction of the optic array is ever processed at the highest resolution. Hinton argues that our brains reason visual information by deconstructing it into a hierarchical representation which we then match to familiar patterns and relationships from memory. The key difference between this understanding and the functionality of CNNs is that recognition of an object should not depend on the angle from which it is viewed. <br />
<br />
To enforce rotational and translational equivariance, Capsule Networks store and preserve hierarchical pose relationships between objects. The core idea behind capsule theory is the explicit numerical representations of relative relationships between different objects within an image. Building these relationships into the Capsule Networks model, the network is able to recognize newly seen objects as a rotated view of a previously seen object. For example, the below image shows the Statue of Liberty under five different angles. If a person had only seen the Statue of Liberty from one angle, they would be able to ascertain that all five pictures below contain the same object (just from a different angle).<br />
<br />
[[File:Rotational Invariance.jpeg |center]]<br />
<br />
Building on this idea of hierarchical representation of spatial relationships between key entities within an image, the authors introduce Capsule Networks. Unlike traditional CNNs, Capsule Networks are better equipped to classify correctly under rotational invariance. Furthermore, the authors managed to achieve state of the art results on MNIST using a fraction of the training samples that alternative state of the art networks require.<br />
<br />
<br />
=Background, Notation, and Definitions=<br />
<br />
==What is a Capsule==<br />
"Each capsule learns to recognize an implicitly defined visual entity over a limited domain of viewing conditions and deformations and it outputs both the probability that the entity is present within its limited domain and a set of “instantiation parameters” that may include the precise pose, lighting and deformation of the visual entity relative to an implicitly defined canonical version of that entity. When the capsule is working properly, the probability of the visual entity being present is locally invariant — it does not change as the entity moves over the manifold of possible appearances within the limited domain covered by the capsule. The instantiation parameters, however, are “equivariant” — as the viewing conditions change and the entity moves over the appearance manifold, the instantiation parameters change by a corresponding amount because they are representing the intrinsic coordinates of the entity on the appearance manifold."<br />
<br />
In essence, capsules store object properties in a vector form; probability of detection is encoded as the vector's length, while spatial properties are encoded as the individual vector components. Thus, when a feature is present but the image captures it under a different angle, the probability of detection remains unchanged.<br />
<br />
A brief overview/understanding of capsules can be found in other papers from the author. To quote from [https://openreview.net/pdf?id=HJWLfGWRb this paper]:<br />
<br />
<blockquote><br />
A capsule network consists of several layers of capsules. The set of capsules in layer L is denoted<br />
as <math>\Omega_L</math>. Each capsule has a 4x4 pose matrix, <math>M</math>, and an activation probability, <math>a</math>. These are like the<br />
activities in a standard neural net: they depend on the current input and are not stored. In between<br />
each capsule i in layer L and each capsule j in layer L + 1 is a 4x4 trainable transformation matrix,<br />
<math>W_{ij}</math> . These <math>W_{ij}</math>'s (and two learned biases per capsule) are the only stored parameters and they<br />
are learned discriminatively. The pose matrix of capsule i is transformed by <math>W_{ij}</math> to cast a vote<br />
<math>V_{ij} = M_iW_{ij}</math> for the pose matrix of capsule j. The poses and activations of all the capsules in layer<br />
L + 1 are calculated by using a non-linear routing procedure which gets as input <math>V_{ij}</math> and <math>a_i</math> for all<br />
<math>i \in \Omega_L, j \in \Omega_{L+1}</math><br />
</blockquote><br />
<math></math><br />
<br />
==Notation==<br />
<br />
We want the length of the output vector of a capsule to represent the probability that the entity represented by the capsule is present in the current input. The paper performs a non-linear squashing operation to ensure that vector length falls between 0 and 1, with shorter vectors (less likely to exist entities) being shrunk towards 0. <br />
<br />
\begin{align} \mathbf{v}_j &= \frac{||\mathbf{s}_j||^2}{1+ ||\mathbf{s}_j||^2} \frac{\mathbf{s}_j}{||\mathbf{s}_j||} \end{align}<br />
<br />
where <math>\mathbf{v}_j</math> is the vector output of capsule <math>j</math> and <math>s_j</math> is its total input.<br />
<br />
For all but the first layer of capsules, the total input to a capsule <math>s_j</math> is a weighted sum over all “prediction vectors” <math>\hat{\mathbf{u}}_{j|i}</math> from the capsules in the layer below and is produced by multiplying the output <math>\mathbf{u}i</math> of a capsule in the layer below by a weight matrix <math>\mathbf{W}ij</math><br />
<br />
\begin{align}<br />
\mathbf{s}_j = \sum_i c_{ij}\hat{\mathbf{u}}_{j|i}, ~\hspace{0.5em} \hat{\mathbf{u}}_{j|i}= \mathbf{W}_{ij}\mathbf{u}_i<br />
\end{align}<br />
where the <math>c_{ij}</math> are coupling coefficients that are determined by the iterative dynamic routing process.<br />
<br />
The coupling coefficients between capsule <math>i</math> and all the capsules in the layer above sum to 1 and are determined by a “routing softmax” whose initial logits <math>b_{ij}</math> are the log prior probabilities that capsule <math>i</math> should be coupled to capsule <math>j</math>.<br />
<br />
\begin{align}<br />
c_{ij} = \frac{\exp(b_{ij})}{\sum_k \exp(b_{ik})}<br />
\end{align}<br />
<br />
=Network Training and Dynamic Routing=<br />
<br />
==Understanding Capsules==<br />
The notation can get somewhat confusing, so I will provide intuition behind the computational steps within a capsule. The following image is taken from naturomic's talk on Capsule Networks.<br />
<br />
[[File:CapsuleNets.jpeg|center|800px]]<br />
<br />
The above image illustrates the key mathematical operations happening within a capsule (and compares them to the structure of a neuron). Although the operations are rather straightforward, it's crucial to note that the capsule computes an affine transformation onto each input vector. The length of the input vectors <math>\mathbf{u}_{i}</math> represent the probability of entity <math>i</math> existing in a lower level. This vector is then reoriented with an affine transform using <math>\mathbf{W}_{ij}</math> matrices that encode spatial relationships between entity <math>\mathbf{u}_{i}</math> and other lower level features.<br />
<br />
We illustrate the intuition behind vector-to-vector matrix multiplication within capsules using the following example: if vectors <math>\mathbf{u}_{1}</math>, <math>\mathbf{u}_{2}</math>, and <math>\mathbf{u}_{3}</math> represent detection of eyes, nose, and mouth respectively, then after multiplication with trained weight matrices <math>\mathbf{W}_{ij}</math> (where j denotes existence of a face), we should get a general idea of the general location of the higher level feature (face), similar to the image below.<br />
<br />
[[File:Predictions.jpeg |center]]<br />
<br />
==Dynamic Routing==<br />
A capsule <math>i</math> in a lower-level layer needs to decide how to send its output vector to higher-level capsules <math>j</math>. This decision is made with probability proportional to <math>c_{ij}</math>. If there are <math>K</math> capsules in the level that capsule <math>i</math> routes to, then we know the following properties about <math>c_{ij}</math>: <math>\sum_{j=1}^M c_{ij} = 1, c_{ij} \geq 0</math><br />
<br />
In essence, the <math>\{c_{ij}\}_{j=1}^M</math> denotes a discrete probability distribution with respect to capsule <math>i</math>'s output location. Lower level capsules decide which higher level capsules to send vectors into by adjusting the corresponding routing weights <math>\{c_{ij}\}_{j=1}^M</math>. After a few iterations in training, numerous vectors will have already been sent to all higher level capsules. Based on the similarity between the current vector being routed and all vectors already sent into the higher level capsules, we decide which capsule to send the current vector into.<br />
[[File:Dynamic Routing.png|center|900px]]<br />
<br />
In the image above, we notice that a cluster of points similar to the current vector has already been routed into capsule K, while most points in capsule J are high dissimilar. It thus makes more sense to route the current observation into capsule K; we adjust the corresponding weight upwards during training.<br />
<br />
These weights are determined through the dynamic routing procedure:<br />
[[File:Routing Algo.png|900px]]<br />
<br />
<br />
Although dynamic routing is not the only manner in which we can encode relationships between capsules, the premise of the paper is to demonstrate the capabilities of capsules under a simple implementation. Since the paper's release in 2017, numerous alternative routing implementations have been released including an EM matrix routing algorithm by the same authors (ICLR 2018).<br />
<br />
=Architecture=<br />
The capsule network architecture given by the authors has 11.36 million trainable parameters. The paper itself is not very detailed on exact implementation of each architectural layer, and hence it leaves some degree of ambiguity on coding various aspects of the original network. The capsule network has 6 overall layers, with the first three layers denoting components of the encoder, and the last 3 denoting components of the decoder.<br />
<br />
==Loss Function==<br />
[[File:Loss Function.png|900px]]<br />
<br />
The cost function looks very complicated, but can be broken down into intuitive components. Before diving into the equation, remember that the length of the vector denotes the probability of object existence. The left side of the equation denotes loss when the network classifies an observation correctly; the term becomes zero when the classification is incorrect. To compute loss when the network correctly classifies the label, we subtract the vector norm from a fixed quantity <math>m^+ := 0.9</math>. On the other hand, when the network classifies a label incorrectly, we penalize the loss based on the network's confidence in the incorrect label; we compute the loss by subtracting <math>m^- := 0.1</math> from the vector norm.<br />
<br />
A graphical representation of loss function values under varying vector norms is given below.<br />
[[File:Loss function chart.png|900px]]<br />
<br />
==Encoder Layers==<br />
All experiments within this paper were conducted on the MNIST dataset, and thus the architecture is built to classify the corresponding dataset. For more complex datasets, the experiments were less promising. <br />
<br />
[[File:Architecture.png|center|900px]]<br />
<br />
The encoder layer takes in a 28x28 MNIST image and learns a 16 dimensional representation of instantiation parameters.<br />
<br />
'''Layer 1: Convolution''': <br />
This layer is a standard convolution layer. Using kernels with size 9x9x1, a stride of 1, and a ReLU activation function, we detect the 2D features within the network.<br />
<br />
'''Layer 2: PrimaryCaps''': <br />
We represent the low level features detected during convolution as 32 primary capsules. Each capsule applies eight convolutional kernels with stride 2 to the output of the convolution layer and feeds the corresponding transformed tensors into the DigiCaps layer.<br />
<br />
'''Layer 3: DigiCaps''': <br />
This layer contains 10 digit capsules, one for each digit. As explained in the dynamic routing procedure, each input vector from the PrimaryCaps layer has its own corresponding weight matrix <math>W_{ij}</math>. Using the routing coefficients <math>c_{ij}</math> and temporary coefficients <math>b_{ij}</math>, we train the DigiCaps layer to output a ten 16 dimensional vectors. The length of the <math>i^{th}</math> vector in this layer corresponds to the probability of detection of digit <math>i</math>.<br />
<br />
==Decoder Layers==<br />
The decoder layer aims to train the capsules to extract meaningful features for image detection/classification. During training, it takes the 16 layer instantiation vector of the correct (not predicted) DigiCaps layer, and attempts to recreate the 28x28 MNIST image as best as possible. Setting the loss function as reconstruction error (Euclidean distance between the reconstructed image and original image), we tune the capsules to encode features that are meaningful within the actual image.<br />
<br />
[[File:Decoder.png|center|900px]]<br />
<br />
The layer consists of three fully connected layers, and transforms a 16x1 vector from the encoder layer into a 28x28 image.<br />
<br />
In addition to the digicaps loss function, we add reconstruction error as a form of regularization. We minimize the Euclidean distance between the outputs of the logistic units and the pixel intensities of the original and reconstructed images. We scale down this reconstruction loss by 0.0005 so that it does not dominate the margin loss during training. As illustrated below, reconstructions from the 16D output of the CapsNet are robust while keeping only important details.<br />
<br />
[[File:Reconstruction.png|center|900px]]<br />
<br />
=MNIST Experimental Results=<br />
<br />
==Accuracy==<br />
The paper tests on the MNIST dataset with 60K training examples, and 10K testing. Wan et al. [2013] achieves 0.21% test error with ensembling and augmenting the data with rotation and scaling. They achieve 0.39% without them. As shown in Table 1, the authors manage to achieve 0.25% test error with only a 3 layer network; the previous state of the art only beat this number with very deep networks. This example shows the importance of routing and reconstruction regularizer, which boosts the performance. On the other hand, while the accuracies are very high, the number of parameters is much smaller compared to the baseline model.<br />
<br />
[[File:Accuracies.png|center|900px]]<br />
<br />
==What Capsules Represent for MNIST==<br />
The following figure shows the digit representation under capsules. Each row shows the reconstruction when one of the 16 dimensions in the DigitCaps representation is tweaked by intervals of 0.05 in the range [−0.25, 0.25]. By tweaking the values, we notice how the reconstruction changes, and thus get a sense for what each dimension is representing. The authors found that some dimensions represent global properties of the digits, while other represent localized properties. <br />
[[File:CapsuleReps.png|center|900px]]<br />
<br />
One example the authors provide is: different dimensions are used for the length of the ascender of a 6 and the size of the loop. The variations include stroke thickness, skew and width, as well as digit-specific variations. The authors are able to show dimension representations using a decoder network by feeding a perturbed vector.<br />
<br />
==Robustness of CapsNet==<br />
The authors conclude that DigitCaps capsules learn more robust representations for each digit class than traditional CNNs. The trained CapsNet becomes moderately robust to small affine transformations in the test data.<br />
<br />
To compare the robustness of CapsNet to affine transformations against traditional CNNs, both models (CapsNet and a traditional CNN with MaxPooling and DropOut) were trained on a padded and translated MNIST training set, in which each example is an MNIST digit placed randomly on a black background of 40 × 40 pixels. The networks were then tested on the [http://www.cs.toronto.edu/~tijmen/affNIST/ affNIST] dataset (MNIST digits with random affine transformation). An under-trained CapsNet which achieved 99.23% accuracy on the MNIST test set achieved a corresponding 79% accuracy on the affnist test set. A traditional CNN achieved similar accuracy (99.22%) on the mnist test set, but only 66% on the affnist test set.<br />
<br />
=MultiMNIST & Other Experiments=<br />
<br />
==MultiMNIST==<br />
To evaluate the performance of the model on highly overlapping digits, the authors generate a 'MultiMNIST' dataset. In MultiMNIST, images are two overlaid MNIST digits of the same set(train or test) but different classes. The results indicate a classification error rate of 5%. Additionally, CapsNet can be used to segment the image into the two digits that compose it. Moreover, the model is able to deal with the overlaps and reconstruct digits correctly since each digit capsule can learn the style from the votes of PrimaryCapsules layer (Figure 5).<br />
<br />
There are some additional steps to generating the MultiMNIST dataset.<br />
<br />
1. Both images are shifted by up to 4 pixels in each direction resulting in a 36 × 36 image. Bounding boxes of digits in MNIST overlap by approximately 80%, so this is used to make both digits identifiable (since there is no RGB difference learnable by the network to separate the digits)<br />
<br />
2. The label becomes a vector of two numbers, representing the original digit and the randomly generated (and overlaid) digit.<br />
<br />
<br />
<br />
[[File:CapsuleNets MultiMNIST.PNG|600px|thumb|center|Figure 5: Sample reconstructions of a CapsNet with 3 routing iterations on MultiMNIST test dataset.<br />
The two reconstructed digits are overlayed in green and red as the lower image. The upper image<br />
shows the input image. L:(l1; l2) represents the label for the two digits in the image and R:(r1; r2)<br />
represents the two digits used for reconstruction. The two right most columns show two examples<br />
with wrong classification reconstructed from the label and from the prediction (P). In the (2; 8)<br />
example the model confuses 8 with a 7 and in (4; 9) it confuses 9 with 0. The other columns have<br />
correct classifications and show that the model accounts for all the pixels while being able to assign<br />
one pixel to two digits in extremely difficult scenarios (column 1 − 4). Note that in dataset generation<br />
the pixel values are clipped at 1. The two columns with the (*) mark show reconstructions from a<br />
digit that is neither the label nor the prediction. These columns suggest that the model is not just<br />
finding the best fit for all the digits in the image including the ones that do not exist. Therefore in case<br />
of (5; 0) it cannot reconstruct a 7 because it knows that there is a 5 and 0 that fit best and account for<br />
all the pixels. Also, in the case of (8; 1) the loop of 8 has not triggered 0 because it is already accounted<br />
for by 8. Therefore it will not assign one pixel to two digits if one of them does not have any other<br />
support.]]<br />
<br />
==Other datasets==<br />
The authors also tested the proposed capsule model on CIFAR10 dataset and achieved an error rate of 10.6%. The model tested was an ensemble of 7 models. Each of the models in the ensemble had the same architecture as the model used for MNIST (apart from 3 additional channels and 64 different types of primary capsules being used). These 7 models were trained on 24x24 patches of the training images for 3 iterations. During experimentation, the authors also found out that adding an additional none-of-the-above category helped improved the overall performance. The error rate achieved is comparable to the error rate achieved by a standard CNN model. According to the authors, one of the reasons for low performance is the fact that background in CIFAR-10 images are too varied for it to be adequately modeled by reasonably sized capsule net.<br />
<br />
The proposed model was also evaluated using a small subset of SVHN dataset. The network trained was much smaller and trained using only 73257 training images. The network still managed to achieve an error rate of 4.3% on the test set.<br />
<br />
=Critique=<br />
Although the network performs incredibly favorably in the author's experiments, it has a long way to go on more complex datasets. On CIFAR 10, the network achieved subpar results, and the experimental results seem to be worse when the problem becomes more complex. This is anticipated, since these networks are still in their early stage; later innovations might come in the upcoming decades/years. It could also be wise to apply the model to other datasets with larger sizes to make the functionality more acceptable. MNIST dataset has simple patterns and even if the model wanted to be presented with only one dataset, it was better not to be MNIST dataset especially in this case that the focus is on human-eye detection and numbers are not that regular in real-life experiences.<br />
<br />
Hinton talks about CapsuleNets revolutionizing areas such as self-driving, but such groundbreaking innovations are far away from CIFAR10, and even further from MNIST. Only time can tell if CapsNets will live up to their hype.<br />
<br />
Capsules inherently segment images and learn a lower dimensional embedding in a new manner, which makes them likely to perform well on segmentation and computer vision tasks once further research is done. <br />
<br />
Additionally, these networks are more interpretable than CNNs, and have strong theoretical reasoning for why they could work. Naturally, it would be hard for a new architecture to beat the heavily researched/modified CNNs.<br />
<br />
* ([https://openreview.net/forum?id=HJWLfGWRb]) it's not fully clear how effective it can be performed / how scalable it is. Evaluation is performed on a small dataset for shape recognition. The approach will need to be tested on larger, more challenging datasets.<br />
<br />
=Future Work=<br />
The same authors [N. F. Geoffrey E Hinton, Sara Sabour] presented another paper "MATRIX CAPSULES WITH EM ROUTING" in ICLR 2018, which achieved better results than the work presented in this paper. They presented a new multi-layered capsule network architecture, implemented an EM routing procedure, and introduced "Coordinate Addition". This new type reduced number of errors by 45%, and performed better than standard CNN on white box adversarial attacks. Capsule architectures are gaining interest because of their ability to achieve equivariance of parts, and employ a new form of pooling called "routing" (as opposed to max pooling) which groups parts that make similar predictions of the whole to which they belong, rather than relying on spatial co-locality.<br />
Moreover, we may try to change the curvature and sensitivities to various factors by introducing new form of loss function. It may improve the performance of the model for more complicated data set which is one of the model's drawback.<br />
<br />
Moreover, as mentioned in critiques, a good future work for this group would be making the model more robust to the dataset and achieve acceptable performance on datasets with more regularly seen images in real life experiences.<br />
<br />
=References=<br />
#N. F. Geoffrey E Hinton, Sara Sabour. Matrix capsules with em routing. In International Conference on Learning Representations, 2018.<br />
#S. Sabour, N. Frosst, and G. E. Hinton, “Dynamic routing between capsules,” arXiv preprint arXiv:1710.09829v2, 2017<br />
# Hinton, G. E., Krizhevsky, A. and Wang, S. D. (2011), Transforming Auto-encoders <br />
#Geoffrey Hinton's talk: What is wrong with convolutional neural nets? - Talk given at MIT. Brain & Cognitive Sciences - Fall Colloquium Series. [https://www.youtube.com/watch?v=rTawFwUvnLE ]<br />
#Understanding Hinton’s Capsule Networks - Max Pechyonkin's series [https://medium.com/ai%C2%B3-theory-practice-business/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b]<br />
#ReferencesMartín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg SCorrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al. Tensorflow: Large-scale machinelearning on heterogeneous distributed systems.arXiv preprint arXiv:1603.04467, 2016.<br />
#Jimmy Ba, Volodymyr Mnih, and Koray Kavukcuoglu. Multiple object recognition with visualattention.arXiv preprint arXiv:1412.7755, 2014.<br />
#Jia-Ren Chang and Yong-Sheng Chen. Batch-normalized maxout network in network.arXiv preprintarXiv:1511.02583, 2015.<br />
#Dan C Cire ̧san, Ueli Meier, Jonathan Masci, Luca M Gambardella, and Jürgen Schmidhuber. High-performance neural networks for visual object classification.arXiv preprint arXiv:1102.0183,2011.<br />
#Ian J Goodfellow, Yaroslav Bulatov, Julian Ibarz, Sacha Arnoud, and Vinay Shet. Multi-digit numberrecognition from street view imagery using deep convolutional neural networks.arXiv preprintarXiv:1312.6082, 2013.</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=CapsuleNets&diff=41840CapsuleNets2018-11-29T16:38:40Z<p>Aghabuss: </p>
<hr />
<div>The paper "Dynamic Routing Between Capsules" was written by three researchers at Google Brain: Sara Sabour, Nicholas Frosst, and Geoffrey E. Hinton. This paper was published and presented at the 31st Conference on Neural Information Processing Systems (NIPS 2017) in Long Beach, California. The same three researchers recently published a highly related paper "Matrix Capsules with EM Routing" for ICLR 2018.<br />
<br />
=Motivation=<br />
<br />
Ever since AlexNet eclipsed the performance of competing architectures in the 2012 ImageNet challenge, convolutional neural networks have maintained their dominance in computer vision applications. Despite the recent successes and innovations brought about by convolutional neural networks, some assumptions made in these networks are perhaps unwarranted and deficient. Using a novel neural network architecture, the authors create CapsuleNets, a network that they claim is able to learn image representations in a more robust, human-like manner. With only a 3 layer capsule network, they achieved near state-of-the-art results on MNIST.<br />
==Adversarial Examples==<br />
<br />
First discussed by Christian Szegedy et. al. in late 2013, adversarial examples have been heavily discussed by the deep learning community as a potential security threat to AI learning. Adversarial examples are defined as inputs that an attacker creates intentionally fool a machine learning model. An example of an adversarial example is shown below: <br />
<br />
[[File:adversarial_img_1.png |center]]<br />
To the human eye, the image appears to be a panda both before and after noise is injected into the image, whereas the trained ConvNet model discerns the noisy image as a Gibbon with almost 100% certainty. The fact that the network is unable to classify the above image as a panda after the epsilon perturbation leads to many potential security risks in AI dependent systems such as self-driving vehicles. Although various methods have been suggested to combat adversarial examples, robust defences are hard to construct due to the inherent difficulties in constructing theoretical models for the adversarial example crafting process. However, beyond the fact that these examples may serve as a security threat, it emphasizes that these convolutional neural networks do not learn image classification/object detection patterns the same way that a human would. Rather than identifying the core features of a panda such as: its eyes, mouth, nose, and the gradient changes in its black/white fur, the convolutional neural network seems to be learning image representations in a completely different manner. Deep learning researchers often attempt to model neural networks after human learning, and it is clear that further steps must be taken to robustify ConvNets against targeted noise perturbations.<br />
<br />
==Drawbacks of CNNs==<br />
Hinton claims that the key fault with traditional CNNs lies within the pooling function. Although pooling builds translational invariance into the network, it fails to preserve spatial relationships between objects. When we pool, we effectively reduce a kxk kernel of convolved cells into a scalar input. This results in a desired local invariance without inhibiting the network's ability to detect features, but causes valuable spatial information to be lost.<br />
<br />
In the example below, the network is able to detect the similar features (eyes, mouth, nose, etc) within both images, but fails to recognize that one image is a human face, while the other is a Picasso-esque due to the CNN's inability to encode spatial relationships after multiple pooling layers.<br />
<br />
<br />
[[File:Equivariance Face.png |center]]<br />
<br />
Conversely, we hope that a CNN can recognize that both of the following pictures contain a kitten. Unfortunately, when we feed the two images into a ResNet50 architecture, only the first image is correctly classified, while the second image is predicted to be a guinea pig.<br />
<br />
<br />
[[File:kitten.jpeg |center]]<br />
<br />
<br />
[[File:kitten-rotated-180.jpg |center]]<br />
<br />
For a more in depth discussion on the problems with ConvNets, please listen to Geoffrey Hinton's talk "What is wrong with convolutional neural nets?" given at MIT during the Brain & Cognitive Sciences - Fall Colloquium Series (December 4, 2014).<br />
<br />
==Intuition for Capsules==<br />
Human vision ignores irrelevant details by using a carefully determined sequence of fixation points to ensure that only a tiny fraction of the optic array is ever processed at the highest resolution. Hinton argues that our brains reason visual information by deconstructing it into a hierarchical representation which we then match to familiar patterns and relationships from memory. The key difference between this understanding and the functionality of CNNs is that recognition of an object should not depend on the angle from which it is viewed. <br />
<br />
To enforce rotational and translational equivariance, Capsule Networks store and preserve hierarchical pose relationships between objects. The core idea behind capsule theory is the explicit numerical representations of relative relationships between different objects within an image. Building these relationships into the Capsule Networks model, the network is able to recognize newly seen objects as a rotated view of a previously seen object. For example, the below image shows the Statue of Liberty under five different angles. If a person had only seen the Statue of Liberty from one angle, they would be able to ascertain that all five pictures below contain the same object (just from a different angle).<br />
<br />
[[File:Rotational Invariance.jpeg |center]]<br />
<br />
Building on this idea of hierarchical representation of spatial relationships between key entities within an image, the authors introduce Capsule Networks. Unlike traditional CNNs, Capsule Networks are better equipped to classify correctly under rotational invariance. Furthermore, the authors managed to achieve state of the art results on MNIST using a fraction of the training samples that alternative state of the art networks require.<br />
<br />
<br />
=Background, Notation, and Definitions=<br />
<br />
==What is a Capsule==<br />
"Each capsule learns to recognize an implicitly defined visual entity over a limited domain of viewing conditions and deformations and it outputs both the probability that the entity is present within its limited domain and a set of “instantiation parameters” that may include the precise pose, lighting and deformation of the visual entity relative to an implicitly defined canonical version of that entity. When the capsule is working properly, the probability of the visual entity being present is locally invariant — it does not change as the entity moves over the manifold of possible appearances within the limited domain covered by the capsule. The instantiation parameters, however, are “equivariant” — as the viewing conditions change and the entity moves over the appearance manifold, the instantiation parameters change by a corresponding amount because they are representing the intrinsic coordinates of the entity on the appearance manifold."<br />
<br />
In essence, capsules store object properties in a vector form; probability of detection is encoded as the vector's length, while spatial properties are encoded as the individual vector components. Thus, when a feature is present but the image captures it under a different angle, the probability of detection remains unchanged.<br />
<br />
A brief overview/understanding of capsules can be found in other papers from the author. To quote from [https://openreview.net/pdf?id=HJWLfGWRb this paper]:<br />
<br />
<blockquote><br />
A capsule network consists of several layers of capsules. The set of capsules in layer L is denoted<br />
as <math>\Omega_L</math>. Each capsule has a 4x4 pose matrix, <math>M</math>, and an activation probability, <math>a</math>. These are like the<br />
activities in a standard neural net: they depend on the current input and are not stored. In between<br />
each capsule i in layer L and each capsule j in layer L + 1 is a 4x4 trainable transformation matrix,<br />
<math>W_{ij}</math> . These <math>W_{ij}</math>'s (and two learned biases per capsule) are the only stored parameters and they<br />
are learned discriminatively. The pose matrix of capsule i is transformed by <math>W_{ij}</math> to cast a vote<br />
<math>V_{ij} = M_iW_{ij}</math> for the pose matrix of capsule j. The poses and activations of all the capsules in layer<br />
L + 1 are calculated by using a non-linear routing procedure which gets as input <math>V_{ij}</math> and <math>a_i</math> for all<br />
<math>i \in \Omega_L, j \in \Omega_{L+1}</math><br />
</blockquote><br />
<math></math><br />
<br />
==Notation==<br />
<br />
We want the length of the output vector of a capsule to represent the probability that the entity represented by the capsule is present in the current input. The paper performs a non-linear squashing operation to ensure that vector length falls between 0 and 1, with shorter vectors (less likely to exist entities) being shrunk towards 0. <br />
<br />
\begin{align} \mathbf{v}_j &= \frac{||\mathbf{s}_j||^2}{1+ ||\mathbf{s}_j||^2} \frac{\mathbf{s}_j}{||\mathbf{s}_j||} \end{align}<br />
<br />
where <math>\mathbf{v}_j</math> is the vector output of capsule <math>j</math> and <math>s_j</math> is its total input.<br />
<br />
For all but the first layer of capsules, the total input to a capsule <math>s_j</math> is a weighted sum over all “prediction vectors” <math>\hat{\mathbf{u}}_{j|i}</math> from the capsules in the layer below and is produced by multiplying the output <math>\mathbf{u}i</math> of a capsule in the layer below by a weight matrix <math>\mathbf{W}ij</math><br />
<br />
\begin{align}<br />
\mathbf{s}_j = \sum_i c_{ij}\hat{\mathbf{u}}_{j|i}, ~\hspace{0.5em} \hat{\mathbf{u}}_{j|i}= \mathbf{W}_{ij}\mathbf{u}_i<br />
\end{align}<br />
where the <math>c_{ij}</math> are coupling coefficients that are determined by the iterative dynamic routing process.<br />
<br />
The coupling coefficients between capsule <math>i</math> and all the capsules in the layer above sum to 1 and are determined by a “routing softmax” whose initial logits <math>b_{ij}</math> are the log prior probabilities that capsule <math>i</math> should be coupled to capsule <math>j</math>.<br />
<br />
\begin{align}<br />
c_{ij} = \frac{\exp(b_{ij})}{\sum_k \exp(b_{ik})}<br />
\end{align}<br />
<br />
=Network Training and Dynamic Routing=<br />
<br />
==Understanding Capsules==<br />
The notation can get somewhat confusing, so I will provide intuition behind the computational steps within a capsule. The following image is taken from naturomic's talk on Capsule Networks.<br />
<br />
[[File:CapsuleNets.jpeg|center|800px]]<br />
<br />
The above image illustrates the key mathematical operations happening within a capsule (and compares them to the structure of a neuron). Although the operations are rather straightforward, it's crucial to note that the capsule computes an affine transformation onto each input vector. The length of the input vectors <math>\mathbf{u}_{i}</math> represent the probability of entity <math>i</math> existing in a lower level. This vector is then reoriented with an affine transform using <math>\mathbf{W}_{ij}</math> matrices that encode spatial relationships between entity <math>\mathbf{u}_{i}</math> and other lower level features.<br />
<br />
We illustrate the intuition behind vector-to-vector matrix multiplication within capsules using the following example: if vectors <math>\mathbf{u}_{1}</math>, <math>\mathbf{u}_{2}</math>, and <math>\mathbf{u}_{3}</math> represent detection of eyes, nose, and mouth respectively, then after multiplication with trained weight matrices <math>\mathbf{W}_{ij}</math> (where j denotes existence of a face), we should get a general idea of the general location of the higher level feature (face), similar to the image below.<br />
<br />
[[File:Predictions.jpeg |center]]<br />
<br />
==Dynamic Routing==<br />
A capsule <math>i</math> in a lower-level layer needs to decide how to send its output vector to higher-level capsules <math>j</math>. This decision is made with probability proportional to <math>c_{ij}</math>. If there are <math>K</math> capsules in the level that capsule <math>i</math> routes to, then we know the following properties about <math>c_{ij}</math>: <math>\sum_{j=1}^M c_{ij} = 1, c_{ij} \geq 0</math><br />
<br />
In essence, the <math>\{c_{ij}\}_{j=1}^M</math> denotes a discrete probability distribution with respect to capsule <math>i</math>'s output location. Lower level capsules decide which higher level capsules to send vectors into by adjusting the corresponding routing weights <math>\{c_{ij}\}_{j=1}^M</math>. After a few iterations in training, numerous vectors will have already been sent to all higher level capsules. Based on the similarity between the current vector being routed and all vectors already sent into the higher level capsules, we decide which capsule to send the current vector into.<br />
[[File:Dynamic Routing.png|center|900px]]<br />
<br />
In the image above, we notice that a cluster of points similar to the current vector has already been routed into capsule K, while most points in capsule J are high dissimilar. It thus makes more sense to route the current observation into capsule K; we adjust the corresponding weight upwards during training.<br />
<br />
These weights are determined through the dynamic routing procedure:<br />
[[File:Routing Algo.png|900px]]<br />
<br />
<br />
Although dynamic routing is not the only manner in which we can encode relationships between capsules, the premise of the paper is to demonstrate the capabilities of capsules under a simple implementation. Since the paper's release in 2017, numerous alternative routing implementations have been released including an EM matrix routing algorithm by the same authors (ICLR 2018).<br />
<br />
=Architecture=<br />
The capsule network architecture given by the authors has 11.36 million trainable parameters. The paper itself is not very detailed on exact implementation of each architectural layer, and hence it leaves some degree of ambiguity on coding various aspects of the original network. The capsule network has 6 overall layers, with the first three layers denoting components of the encoder, and the last 3 denoting components of the decoder.<br />
<br />
==Loss Function==<br />
[[File:Loss Function.png|900px]]<br />
<br />
The cost function looks very complicated, but can be broken down into intuitive components. Before diving into the equation, remember that the length of the vector denotes the probability of object existence. The left side of the equation denotes loss when the network classifies an observation correctly; the term becomes zero when the classification is incorrect. To compute loss when the network correctly classifies the label, we subtract the vector norm from a fixed quantity <math>m^+ := 0.9</math>. On the other hand, when the network classifies a label incorrectly, we penalize the loss based on the network's confidence in the incorrect label; we compute the loss by subtracting <math>m^- := 0.1</math> from the vector norm.<br />
<br />
A graphical representation of loss function values under varying vector norms is given below.<br />
[[File:Loss function chart.png|900px]]<br />
<br />
==Encoder Layers==<br />
All experiments within this paper were conducted on the MNIST dataset, and thus the architecture is built to classify the corresponding dataset. For more complex datasets, the experiments were less promising. <br />
<br />
[[File:Architecture.png|center|900px]]<br />
<br />
The encoder layer takes in a 28x28 MNIST image and learns a 16 dimensional representation of instantiation parameters.<br />
<br />
'''Layer 1: Convolution''': <br />
This layer is a standard convolution layer. Using kernels with size 9x9x1, a stride of 1, and a ReLU activation function, we detect the 2D features within the network.<br />
<br />
'''Layer 2: PrimaryCaps''': <br />
We represent the low level features detected during convolution as 32 primary capsules. Each capsule applies eight convolutional kernels with stride 2 to the output of the convolution layer and feeds the corresponding transformed tensors into the DigiCaps layer.<br />
<br />
'''Layer 3: DigiCaps''': <br />
This layer contains 10 digit capsules, one for each digit. As explained in the dynamic routing procedure, each input vector from the PrimaryCaps layer has its own corresponding weight matrix <math>W_{ij}</math>. Using the routing coefficients <math>c_{ij}</math> and temporary coefficients <math>b_{ij}</math>, we train the DigiCaps layer to output a ten 16 dimensional vectors. The length of the <math>i^{th}</math> vector in this layer corresponds to the probability of detection of digit <math>i</math>.<br />
<br />
==Decoder Layers==<br />
The decoder layer aims to train the capsules to extract meaningful features for image detection/classification. During training, it takes the 16 layer instantiation vector of the correct (not predicted) DigiCaps layer, and attempts to recreate the 28x28 MNIST image as best as possible. Setting the loss function as reconstruction error (Euclidean distance between the reconstructed image and original image), we tune the capsules to encode features that are meaningful within the actual image.<br />
<br />
[[File:Decoder.png|center|900px]]<br />
<br />
The layer consists of three fully connected layers, and transforms a 16x1 vector from the encoder layer into a 28x28 image.<br />
<br />
In addition to the digicaps loss function, we add reconstruction error as a form of regularization. We minimize the Euclidean distance between the outputs of the logistic units and the pixel intensities of the original and reconstructed images. We scale down this reconstruction loss by 0.0005 so that it does not dominate the margin loss during training. As illustrated below, reconstructions from the 16D output of the CapsNet are robust while keeping only important details.<br />
<br />
[[File:Reconstruction.png|center|900px]]<br />
<br />
=MNIST Experimental Results=<br />
<br />
==Accuracy==<br />
The paper tests on the MNIST dataset with 60K training examples, and 10K testing. Wan et al. [2013] achieves 0.21% test error with ensembling and augmenting the data with rotation and scaling. They achieve 0.39% without them. As shown in Table 1, the authors manage to achieve 0.25% test error with only a 3 layer network; the previous state of the art only beat this number with very deep networks. This example shows the importance of routing and reconstruction regularizer, which boosts the performance. On the other hand, while the accuracies are very high, the number of parameters is much smaller compared to the baseline model.<br />
<br />
[[File:Accuracies.png|center|900px]]<br />
<br />
==What Capsules Represent for MNIST==<br />
The following figure shows the digit representation under capsules. Each row shows the reconstruction when one of the 16 dimensions in the DigitCaps representation is tweaked by intervals of 0.05 in the range [−0.25, 0.25]. By tweaking the values, we notice how the reconstruction changes, and thus get a sense for what each dimension is representing. The authors found that some dimensions represent global properties of the digits, while other represent localized properties. <br />
[[File:CapsuleReps.png|center|900px]]<br />
<br />
One example the authors provide is: different dimensions are used for the length of the ascender of a 6 and the size of the loop. The variations include stroke thickness, skew and width, as well as digit-specific variations. The authors are able to show dimension representations using a decoder network by feeding a perturbed vector.<br />
<br />
==Robustness of CapsNet==<br />
The authors conclude that DigitCaps capsules learn more robust representations for each digit class than traditional CNNs. The trained CapsNet becomes moderately robust to small affine transformations in the test data.<br />
<br />
To compare the robustness of CapsNet to affine transformations against traditional CNNs, both models (CapsNet and a traditional CNN with MaxPooling and DropOut) were trained on a padded and translated MNIST training set, in which each example is an MNIST digit placed randomly on a black background of 40 × 40 pixels. The networks were then tested on the [http://www.cs.toronto.edu/~tijmen/affNIST/ affNIST] dataset (MNIST digits with random affine transformation). An under-trained CapsNet which achieved 99.23% accuracy on the MNIST test set achieved a corresponding 79% accuracy on the affnist test set. A traditional CNN achieved similar accuracy (99.22%) on the mnist test set, but only 66% on the affnist test set.<br />
<br />
=MultiMNIST & Other Experiments=<br />
<br />
==MultiMNIST==<br />
To evaluate the performance of the model on highly overlapping digits, the authors generate a 'MultiMNIST' dataset. In MultiMNIST, images are two overlaid MNIST digits of the same set(train or test) but different classes. The results indicate a classification error rate of 5%. Additionally, CapsNet can be used to segment the image into the two digits that compose it. Moreover, the model is able to deal with the overlaps and reconstruct digits correctly since each digit capsule can learn the style from the votes of PrimaryCapsules layer (Figure 5).<br />
<br />
There are some additional steps to generating the MultiMNIST dataset.<br />
<br />
1. Both images are shifted by up to 4 pixels in each direction resulting in a 36 × 36 image. Bounding boxes of digits in MNIST overlap by approximately 80%, so this is used to make both digits identifiable (since there is no RGB difference learnable by the network to separate the digits)<br />
<br />
2. The label becomes a vector of two numbers, representing the original digit and the randomly generated (and overlaid) digit.<br />
<br />
<br />
<br />
[[File:CapsuleNets MultiMNIST.PNG|600px|thumb|center|Figure 5: Sample reconstructions of a CapsNet with 3 routing iterations on MultiMNIST test dataset.<br />
The two reconstructed digits are overlayed in green and red as the lower image. The upper image<br />
shows the input image. L:(l1; l2) represents the label for the two digits in the image and R:(r1; r2)<br />
represents the two digits used for reconstruction. The two right most columns show two examples<br />
with wrong classification reconstructed from the label and from the prediction (P). In the (2; 8)<br />
example the model confuses 8 with a 7 and in (4; 9) it confuses 9 with 0. The other columns have<br />
correct classifications and show that the model accounts for all the pixels while being able to assign<br />
one pixel to two digits in extremely difficult scenarios (column 1 − 4). Note that in dataset generation<br />
the pixel values are clipped at 1. The two columns with the (*) mark show reconstructions from a<br />
digit that is neither the label nor the prediction. These columns suggest that the model is not just<br />
finding the best fit for all the digits in the image including the ones that do not exist. Therefore in case<br />
of (5; 0) it cannot reconstruct a 7 because it knows that there is a 5 and 0 that fit best and account for<br />
all the pixels. Also, in the case of (8; 1) the loop of 8 has not triggered 0 because it is already accounted<br />
for by 8. Therefore it will not assign one pixel to two digits if one of them does not have any other<br />
support.]]<br />
<br />
==Other datasets==<br />
The authors also tested the proposed capsule model on CIFAR10 dataset and achieved an error rate of 10.6%. The model tested was an ensemble of 7 models. Each of the models in the ensemble had the same architecture as the model used for MNIST (apart from 3 additional channels and 64 different types of primary capsules being used). These 7 models were trained on 24x24 patches of the training images for 3 iterations. During experimentation, the authors also found out that adding an additional none-of-the-above category helped improved the overall performance. The error rate achieved is comparable to the error rate achieved by a standard CNN model. According to the authors, one of the reasons for low performance is the fact that background in CIFAR-10 images are too varied for it to be adequately modeled by reasonably sized capsule net.<br />
<br />
The proposed model was also evaluated using a small subset of SVHN dataset. The network trained was much smaller and trained using only 73257 training images. The network still managed to achieve an error rate of 4.3% on the test set.<br />
<br />
=Critique=<br />
Although the network performs incredibly favorably in the author's experiments, it has a long way to go on more complex datasets. On CIFAR 10, the network achieved subpar results, and the experimental results seem to be worse when the problem becomes more complex. This is anticipated, since these networks are still in their early stage; later innovations might come in the upcoming decades/years. It could also be wise to apply the model to other datasets with larger sizes to make the functionality more acceptable. MNIST dataset has simple patterns and even if the model wanted to be presented with only one dataset, it was better not to be MNIST dataset especially in this case that the focus is on human-eye detection and numbers are not that regular in real-life experiences.<br />
<br />
Hinton talks about CapsuleNets revolutionizing areas such as self-driving, but such groundbreaking innovations are far away from CIFAR10, and even further from MNIST. Only time can tell if CapsNets will live up to their hype.<br />
<br />
Capsules inherently segment images and learn a lower dimensional embedding in a new manner, which makes them likely to perform well on segmentation and computer vision tasks once further research is done. <br />
<br />
Additionally, these networks are more interpretable than CNNs, and have strong theoretical reasoning for why they could work. Naturally, it would be hard for a new architecture to beat the heavily researched/modified CNNs.<br />
<br />
* ([https://openreview.net/forum?id=HJWLfGWRb]) it's not fully clear how effective it can be performed / how scalable it is. Evaluation is performed on a small dataset for shape recognition. The approach will need to be tested on larger, more challenging datasets.<br />
<br />
=Future Work=<br />
The same authors [N. F. Geoffrey E Hinton, Sara Sabour] presented another paper "MATRIX CAPSULES WITH EM ROUTING" in ICLR 2018, which achieved better results than the work presented in this paper. They presented a new multi-layered capsule network architecture, implemented an EM routing procedure, and introduced "Coordinate Addition". This new type reduced number of errors by 45%, and performed better than standard CNN on white box adversarial attacks. Capsule architectures are gaining interest because of their ability to achieve equivariance of parts, and employ a new form of pooling called "routing" (as opposed to max pooling) which groups parts that make similar predictions of the whole to which they belong, rather than relying on spatial co-locality.<br />
Moreover, we may try to change the curvature and sensitivities to various factors by introducing new form of loss function. It may improve the performance of the model for more complicated data set which is one of the model's drawback.<br />
<br />
Moreover, as mentioned in critiques, a good future work for this group would be making the model more robust to the dataset and achieve acceptable performance on datasets with more regularly seen images in real life experiences.<br />
<br />
=References=<br />
#N. F. Geoffrey E Hinton, Sara Sabour. Matrix capsules with em routing. In International Conference on Learning Representations, 2018.<br />
#S. Sabour, N. Frosst, and G. E. Hinton, “Dynamic routing between capsules,” arXiv preprint arXiv:1710.09829v2, 2017<br />
# Hinton, G. E., Krizhevsky, A. and Wang, S. D. (2011), Transforming Auto-encoders <br />
#Geoffrey Hinton's talk: What is wrong with convolutional neural nets? - Talk given at MIT. Brain & Cognitive Sciences - Fall Colloquium Series. [https://www.youtube.com/watch?v=rTawFwUvnLE ]<br />
#Understanding Hinton’s Capsule Networks - Max Pechyonkin's series [https://medium.com/ai%C2%B3-theory-practice-business/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b]</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946F18/Autoregressive_Convolutional_Neural_Networks_for_Asynchronous_Time_Series&diff=41658stat946F18/Autoregressive Convolutional Neural Networks for Asynchronous Time Series2018-11-27T21:51:57Z<p>Aghabuss: </p>
<hr />
<div>This page is a summary of the paper "[http://proceedings.mlr.press/v80/binkowski18a/binkowski18a.pdf Autoregressive Convolutional Neural Networks for Asynchronous Time Series]" by Mikołaj Binkowski, Gautier Marti, Philippe Donnat. It was published at ICML in 2018. The code for this paper is provided [https://github.com/mbinkowski/nntimeseries here].<br />
<br />
=Introduction=<br />
In this paper, the authors propose a deep convolutional network architecture called Significance-Offset Convolutional Neural Network for regression of multivariate asynchronous time series. The model is inspired by standard autoregressive(AR) models and gating systems used in recurrent neural networks. The model is evaluated on various time series data including:<br />
# Hedge fund proprietary dataset of over 2 million quotes for a credit derivative index, <br />
# An artificially generated noisy auto-regressive series, <br />
# A UCI household electricity consumption dataset. <br />
<br />
This paper focuses on time series with multi-variate and noisy signals, especially financial data. Financial time series is challenging to predict due to their low signal-to-noise ratio and heavy-tailed distributions. For example, the same signal (e.g. price of a stock) is obtained from different sources (e.g. financial news, an investment bank, financial analyst etc.) asynchronously. Each source may have a different bias or noise. (Figure 1) The investment bank with more clients can update their information more precisely than the investment bank with fewer clients, then the significance of each past observations may depend on other factors that change in time. Therefore, the traditional econometric models such as AR, VAR, VARMA[1] might not be sufficient. However, their relatively good performance could allow us to combine such linear econometric models with deep neural networks that can learn highly nonlinear relationships. This model is inspired by the gating mechanism which is successful in RNNs and Highway Networks.<br />
<br />
The time series forecasting problem can be expressed as a conditional probability distribution below,<br />
<div style="text-align: center;"><math>p(X_{t+d}|X_t,X_{t-1},...) = f(X_t,X_{t-1},...)</math></div><br />
Thus, we focus on modeling the predictors of future values of time series given their past values. <br />
The predictability of financial dataset still remains an open problem and is discussed in various publications [2].<br />
<br />
[[File:Junyi1.png | 500px|thumb|center|Figure 1: Quotes from four different market participants (sources) for the same credit default swaps (CDS) throughout one day. Each trader displays from time to time the prices for which he offers to buy (bid) and sell (ask) the underlying CDS. The filled area marks the difference between the best sell and buy offers (spread) at each time.]]<br />
<br />
The paper also provides empirical evidence that their model which combines linear models with deep learning models could perform better than just DL models like CNN, LSTMs and Phased LSTMs.<br />
<br />
=Related Work=<br />
===Time series forecasting===<br />
From recent proceedings in main machine learning venues i.e. ICML, NIPS, AISTATS, UAI, we can notice that time series are often forecast using Gaussian processes[3,4], especially for irregularly sampled time series[5]. Though still largely independent, combined models have started to appear, for example, the Gaussian Copula Process Volatility model[6]. For this paper, the authors use coupling AR models and neural networks to achieve such combined models.<br />
<br />
Although deep neural networks have been applied into many fields and produced satisfactory results, there still is little literature on deep learning for time series forecasting. More recently, the papers include Sirignano (2016)[7] that used 4-layer perceptrons in modeling price change distributions in Limit Order Books, and Borovykh et al. (2017)[8] who applied more recent WaveNet architecture to several short univariate and bivariate time-series (including financial ones). Heaton et al. (2016)[9] claimed to use autoencoders with a single hidden layer to compress multivariate financial data. Neil et al. (2016)[10] presented augmentation of LSTM architecture suitable for asynchronous series, which stimulates learning dependencies of different frequencies through time gate. <br />
<br />
In this paper, the authors examine the capabilities of several architectures (CNN, residual network, multi-layer LSTM, and phase LSTM) on AR-like artificial asynchronous and noisy time series, household electricity consumption dataset, and on real financial data from the credit default swap market with some inefficiencies.<br />
<br />
===Gating and weighting mechanisms===<br />
Gating mechanisms for neural networks has ability to overcome the problem of vanishing gradient, and can be expressed as <math display="inline">f(x)=c(x) \otimes \sigma(x)</math>, where <math>f</math> is the output function, <math>c</math> is a "candidate output" (a nonlinear function of <math>x</math>), <math>\otimes</math> is an element-wise matrix product, and <math>\sigma : \mathbb{R} \rightarrow [0,1] </math> is a sigmoid nonlinearity that controls the amount of output passed to the next layer. This composition of functions may lead to popular recurrent architecture such as LSTM and GRU[11].<br />
<br />
The idea of the gating system is aimed to weight outputs of the intermediate layers within neural networks, and is most closely related to softmax gating used in MuFuRu(Multi-Function Recurrent Unit)[12], i.e.<br />
<math display="inline"> f(x) = \sum_{l=1}^L p^l(x) \otimes f^l(x), p(x)=softmax(\widehat{p}(x)), </math>, where <math>(f^l)_{l=1}^L </math>are candidate outputs(composition operators in MuFuRu), <math>(\widehat{p}^l)_{l=1}^L </math>are linear functions of inputs. <br />
<br />
This idea is also successfully used in attention networks[13] such as image captioning and machine translation. In this paper, the method is similar as this. The difference is that modeling the functions as multi-layer CNNs. Another difference is that not using recurrent layers, which can enable the network to remember the parts of the sentence/image already translated/described.<br />
<br />
=Motivation=<br />
There are mainly five motivations they stated in the paper:<br />
#The forecasting problem in this paper has done almost independently by econometrics and machine learning communities. Unlike in machine learning, research in econometrics are more likely to explain variables rather than improving out-of-sample prediction power. These models tend to 'over-fit' on financial time series, their parameters are unstable and have poor performance on out-of-sample prediction.<br />
#Although Gaussian processes provide a useful theoretical framework that is able to handle asynchronous data, they often follow heavy-tailed distribution for financial datasets.<br />
#Predictions of autoregressive time series may involve highly nonlinear functions if sampled irregularly. For AR time series with higher order and have more past observations, the expectation of it <math display="inline">\mathbb{E}[X(t)|{X(t-m), m=1,...,M}]</math> may involve more complicated functions that in general may not allow closed-form expression.<br />
#In practice, the dimensions of multivariate time series are often observed separately and asynchronously, such series at fixed frequency may lead to lose information or enlarge the dataset, which is shown in Figure 2(a). Therefore, the core of the proposed architecture SOCNN represents separate dimensions as a single one with dimension and duration indicators as additional features(Figure 2(b)).<br />
#Given a series of pairs of consecutive input values and corresponding durations, <math display="inline"> x_n = (X(t_n),t_n-t_{n-1}) </math>. One may expect that LSTM may memorize the input values in each step and weight them at the output according to the duration, but this approach may lead to an imbalance between the needs for memory and for linearity. The weights that are assigned to the memorized observations potentially require several layers of nonlinearity to be computed properly, while past observations might just need to be memorized as they are.<br />
<br />
[[File:Junyi2.png | 550px|thumb|center|Figure 2: (a) Fixed sampling frequency and its drawbacks; keep- ing all available information leads to much more datapoints. (b) Proposed data representation for the asynchronous series. Consecutive observations are stored together as a single value series, regardless of which series they belong to; this information, however, is stored in indicator features, alongside durations between observations.]]<br />
<br />
<br />
=Model Architecture=<br />
Suppose there's a multivariate time series <math display="inline">(x_n)_{n=0}^{\infty} \subset \mathbb{R}^d </math>, we want to predict the conditional future values of a subset of elements of <math>x_n</math><br />
<div style="text-align: center;"><math>y_n = \mathbb{E} [x_n^I | {x_{n-m}, m=1,2,...}], </math></div><br />
where <math> I=\{i_1,i_2,...i_{d_I}\} \subset \{1,2,...,d\} </math> is a subset of features of <math>x_n</math>.<br />
Let <math> \textbf{x}_n^{-M} = (x_{n-m})_{m=1}^M </math>. The estimator of <math>y_n</math> can be expressed as:<br />
<div style="text-align: center;"><math>\hat{y}_n = \sum_{m=1}^M [F(\textbf{x}_n^{-M}) \otimes \sigma(S(\textbf{x}_n^{-M}))].,_m ,</math></div><br />
This is summation of the columns of the matrix in bracket, where<br />
#<math>F,S : \mathbb{R}^{d \times M} \rightarrow \mathbb{R}^{d_I \times M}</math> are neural networks. S is a fully convolutional network which is composed of convolutional layers only. <math>F</math> is in the form of<br />
<math display="inline">F(\textbf{x}_n^{-M}) = W \otimes [off(x_{n-m}) + x_{n-m}^I)]_{m=1}^M </math> where <math> W \in \mathbb{R}^{d_I \times M}</math> and <math> off: \mathbb{R}^d \rightarrow \mathbb{R}^{d_I} </math> is a multilayer perceptron.<br />
#<math>\sigma</math> is a normalized activation function independent at each row, i.e. <math display="inline"> \sigma ((a_1^T,...,a_{d_I}^T)^T)=(\sigma(a_1)^T,...\sigma(a_{d_I})^T)^T </math><br />
# <math>\otimes</math> is element-wise matrix multiplication.<br />
#<math>A.,_m</math> denotes the m-th column of a matrix A, and <math>\sum_{m=1}^M A.,_m=A(1,1,...,1)^T</math>.<br />
Since <math>\sum_{m=1}^M W.,_m=W(1,1,...,1)^T</math> and <math>\sum_{m=1}^M S.,_m=S(1,1,...,1)^T</math>, we can express <math>\hat{y}_n</math> as:<br />
<div style="text-align: center;"><math>\hat{y}_n = \sum_{m=1}^M W.,_m \otimes (off(x_{n-m}) + x_{n-m}^I) \otimes \sigma(S.,_m(\textbf{x}_n^{-M}))</math></div><br />
This is the proposed network, Significance-Offset Convolutional Neural Network, <math>off</math> and <math>S</math> in the equation are corresponding to Offset and Significance in the name respectively.<br />
Figure 3 shows the scheme of network.<br />
<br />
[[File:Junyi3.png | 600px|thumb|center|Figure 3: A scheme of the proposed SOCNN architecture. The network preserves the time-dimension up to the top layer, while the number of features per timestep (filters) in the hidden layers is custom. The last convolutional layer, however, has the number of filters equal to dimension of the output. The Weighting frame shows how outputs from offset and significance networks are combined in accordance with Eq. of <math>\hat{y}_n</math>.]]<br />
<br />
The form of <math>\hat{y}_n</math> forced to separate the temporal dependence (obtained in weights <math>W_m</math>). S is determined by its filters which capture local dependencies and are independent of the relative position in time, the predictors <math>off(x_{n-m})</math> are completely independent of position in time. An adjusted single regressor for the target variable is provided by each past observation through the offset network. Since in asynchronous sampling procedure, consecutive values of x come from different signals and might be heterogeneous, therefore adjustment of offset network is important. In addition, significance network provides data-dependent weight for each regressor and sums them up in an autoregressive manner.<br />
<br />
===Relation to asynchronous data===<br />
One common problem of time series is that durations are varying between consecutive observations, the paper states two ways to solve this problem<br />
#Data preprocessing: aligning the observations at some fixed frequency e.g. duplicating and interpolating observations as shown in Figure 2(a). However, as mentioned in the figure, this approach will tend to loss of information and enlarge the size of the dataset and model complexity.<br />
#Add additional features: Treating the duration or time of the observations as additional features, it is the core of SOCNN, which is shown in Figure 2(b).<br />
<br />
===Loss function===<br />
The output of the offset network is series of separate predictors of changes between corresponding observations <math>x_{n-m}^I</math> and the target value<math>y_n</math>, this is the reason why we use auxiliary loss function, which equals to mean squared error of such intermediate predictions:<br />
<div style="text-align: center;"><math>L^{aux}(\textbf{x}_n^{-M}, y_n)=\frac{1}{M} \sum_{m=1}^M ||off(x_{n-m}) + x_{n-m}^I -y_n||^2 </math></div><br />
The total loss for the sample <math> \textbf{x}_n^{-M},y_n) </math> is then given by:<br />
<div style="text-align: center;"><math>L^{tot}(\textbf{x}_n^{-M}, y_n)=L^2(\widehat{y}_n, y_n)+\alpha L^{aux}(\textbf{x}_n^{-M}, y_n)</math></div><br />
where <math>\widehat{y}_n</math> was mentioned before, <math>\alpha \geq 0</math> is a constant.<br />
<br />
=Experiments=<br />
The paper evaluated SOCNN architecture on three datasets: artificially generated datasets, [https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption household electric power consumption dataset], and the financial dataset of bid/ask quotes sent by several market participants active in the credit derivatives market. Comparing its performance with simple CNN, single and multiplayer LSTM and 25-layer ResNet. Apart from the evaluation of the SOCNN architecture the paper also discusses the impact of network components such as: such as auxiliary<br />
loss and the depth of the offset sub-network. The code and datasets are available [https://github.com/mbinkowski/nntimeseries here]<br />
<br />
==Datasets==<br />
Artificial data: They generated 4 artificial series, <math> X_{K \times N}</math>, where <math>K \in \{16,64\} </math>. Therefore there is a synchronous and an asynchronous series for each K value.<br />
<br />
Electricity data: This UCI dataset contains 7 different features excluding date and time. The features include global active power, global reactive power, voltage, global intensity, sub-metering 1, sub-metering 2 and sub-metering 3, recorded every minute for 47 months. The data has been altered so that one observation contains only one value of 7 features, while durations between consecutive observations are ranged from 1 to 7 minutes. The goal is to predict all 7 features for the next time step.<br />
<br />
Non-anonymous quotes: The dataset contains 2.1 million quotes from 28 different sources from different market participants such as analysts, banks etc. Each quote is characterized by 31 features: the offered price, 28 indicators of the quoting source, the direction indicator (the quote refers to either a buy or a sell offer) and duration from the previous quote. For each source and direction, we want to predict the next quoted price from this given source and direction considering the last 60 quotes.<br />
<br />
==Training details==<br />
They applied grid search on some hyperparameters in order to get the significance of its components. The hyperparameters include the offset sub-network's depth and the auxiliary weight <math>\alpha</math>. For offset sub-network's depth, they use 1, 10,1 for artificial, electricity and quotes dataset respectively; and they compared the values of <math>\alpha</math> in {0,0.1,0.01}.<br />
<br />
They chose LeakyReLU as activation function for all networks:<br />
<div style="text-align: center;"><math>\sigma^{LeakyReLU}(x) = x</math> if <math>x\geq 0</math>, and <math>0.1x</math> otherwise </div><br />
They use the same number of layers, same stride and similar kernel size structure in CNN. In each trained CNN, they applied max pooling with the pool size of 2 every 2 convolutional layers.<br />
<br />
Table 1 presents the configuration of network hyperparameters used in comparison<br />
<br />
[[File:Junyi4.png | 400px|center|]]<br />
<br />
===Network Training===<br />
The training and validation data were sampled randomly from the first 80% of timesteps in each series, with ratio of 3 to 1. The remaining 20% of data was used as a test set.<br />
<br />
All models were trained using Adam optimizer because the authors found that its rate of convergence was much faster than standard Stochastic Gradient Descent in early tests.<br />
<br />
They used a batch size of 128 for artificial and electricity data, and 256 for quotes dataset, and applied batch normalization between each convolution and the following activation. <br />
<br />
At the beginning of each epoch, the training samples were randomly sampled. To prevent overfitting, they applied dropout and early stopping.<br />
<br />
Weights were initialized using the normalized uniform procedure proposed by Glorot & Bengio (2010).[14]<br />
<br />
The authors carried out the experiments on Tensorflow and Keras and used different GPU to optimize the model for different datasets.<br />
<br />
==Results==<br />
Table 2 shows all results performed from all datasets.<br />
[[File:Junyi5.png | 600px|center|]]<br />
We can see that SOCNN outperforms in all asynchronous artificial, electricity and quotes datasets. For synchronous data, LSTM might be slightly better, but SOCNN almost has the same results with LSTM. Phased LSTM and ResNet have performed really bad on artificial asynchronous dataset and quotes dataset respectively. Notice that having more than one layer of offset network would have negative impact on results. Also, the higher weights of auxiliary loss(<math>\alpha</math>considerably improved the test error on asynchronous dataset, see Table 3. However, for other datasets, its impact was negligible.<br />
[[File:Junyi6.png | 400px|center|]]<br />
In general, SOCNN has significantly lower variance of the test and validation errors, especially in the early stage of the training process and for quotes dataset. This effect can be seen in the learning curves for Asynchronous 64 artificial dataset presented in Figure 5.<br />
[[File:Junyi7.png | 500px|thumb|center|Figure 5: Learning curves with different auxiliary weights for SOCNN model trained on Asynchronous 64 dataset. The solid lines indicate the test error while the dashed lines indicate the training error.]]<br />
<br />
Finally, we want to test the robustness of the proposed model SOCNN, adding noise terms to asynchronous 16 dataset and check how these networks perform. The result is shown in Figure 6.<br />
[[File:Junyi8.png | 600px|thumb|center|Figure 6: Experiment comparing robustness of the considered networks for Asynchronous 16 dataset. The plots show how the error would change if an additional noise term was added to the input series. The dotted curves show the total significance and average absolute offset (not to scale) outputs for the noisy observations. Interestingly, the significance of the noisy observations increases with the magnitude of noise; i.e. noisy observations are far from being discarded by SOCNN.]]<br />
From Figure 6, the purple line and green line seems staying at the same position in training and testing process. SOCNN and single-layer LSTM are most robust compared to other networks, and least prone to overfitting.<br />
<br />
=Conclusion and Discussion=<br />
In this paper, the authors have proposed a new architecture called Significance-Offset Convolutional Neural Network, which combines AR-like weighting mechanism and convolutional neural network. This new architecture is designed for high-noise asynchronous time series and achieves outperformance in forecasting several asynchronous time series compared to popular convolutional and recurrent networks. <br />
<br />
The SOCNN can be extended further by adding intermediate weighting layers of the same type in the network structure. Another possible extension but needs further empirical studies is that we consider not just <math>1 \times 1</math> convolutional kernels on the offset sub-network. Also, this new architecture might be tested on other real-life datasets with relevant characteristics in the future, especially on econometric datasets and more generally for time series (stochastic processes) regression.<br />
<br />
=Critiques=<br />
#The paper is most likely an application paper, and the proposed new architecture shows improved performance over baselines in the asynchronous time series.<br />
#The quote data cannot be reached, only two datasets available.<br />
#The 'Significance' network was described as critical to the model in paper, but they did not show how the performance of SOCNN with respect to the significance network.<br />
#The transform of the original data to asynchronous data is not clear.<br />
#The experiments on the main application are not reproducible because the data is proprietary.<br />
#The way that train and test data were split is unclear. This could be important in the case of the financial data set.<br />
#Although the auxiliary loss function was mentioned as an important part, the advantages of it was not too clear in the paper. Maybe it is better that the paper describes a little more about its effectiveness.<br />
#It was not mentioned clearly in the paper whether the model training was done on a rolling basis for time series forecasting.<br />
#The noise term used in section 5's model robustness analysis uses evenly distributed noise (see Appendix B). While the analysis is a good start, analysis with different noise distributions would make the findings more generalizable.<br />
#The paper uses financial/economic data as one of its testing data set. Instead of comparing neural network models such as CNN which is known to work badly on time series data, it would be much better if the author compared to well-known econometric time series models such as GARCH and VAR.<br />
<br />
=References=<br />
[1] Hamilton, J. D. Time series analysis, volume 2. Princeton university press Princeton, 1994. <br />
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[2] Fama, E. F. Efficient capital markets: A review of theory and empirical work. The journal of Finance, 25(2):383–417, 1970.<br />
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[3] Petelin, D., Sˇindela ́ˇr, J., Pˇrikryl, J., and Kocijan, J. Financial modeling using gaussian process models. In Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), 2011 IEEE 6th International Conference on, volume 2, pp. 672–677. IEEE, 2011.<br />
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[4] Tobar, F., Bui, T. D., and Turner, R. E. Learning stationary time series using gaussian processes with nonparametric kernels. In Advances in Neural Information Processing Systems, pp. 3501–3509, 2015.<br />
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[5] Hwang, Y., Tong, A., and Choi, J. Automatic construction of nonparametric relational regression models for multiple time series. In Proceedings of the 33rd International Conference on Machine Learning, 2016.<br />
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[6] Wilson, A. and Ghahramani, Z. Copula processes. In Advances in Neural Information Processing Systems, pp. 2460–2468, 2010.<br />
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[7] Sirignano, J. Extended abstract: Neural networks for limit order books, February 2016.<br />
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[8] Borovykh, A., Bohte, S., and Oosterlee, C. W. Condi- tional time series forecasting with convolutional neural networks, March 2017.<br />
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[9] Heaton, J. B., Polson, N. G., and Witte, J. H. Deep learn- ing in finance, February 2016.<br />
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[10] Neil, D., Pfeiffer, M., and Liu, S.-C. Phased lstm: Acceler- ating recurrent network training for long or event-based sequences. In Advances In Neural Information Process- ing Systems, pp. 3882–3890, 2016.<br />
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[11] Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. Em- pirical evaluation of gated recurrent neural networks on sequence modeling, December 2014.<br />
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[12] Weissenborn, D. and Rockta ̈schel, T. MuFuRU: The Multi-Function recurrent unit, June 2016.<br />
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[13] Cho, K., Courville, A., and Bengio, Y. Describing multi- media content using attention-based Encoder–Decoder networks. IEEE Transactions on Multimedia, 17(11): 1875–1886, July 2015. ISSN 1520-9210.<br />
<br />
[14] Glorot, X. and Bengio, Y. Understanding the dif- ficulty of training deep feedforward neural net- works. In In Proceedings of the International Con- ference on Artificial Intelligence and Statistics (AIS- TATSaˆ10). Society for Artificial Intelligence and Statistics, 2010.</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Mapping_Images_to_Scene_Graphs_with_Permutation-Invariant_Structured_Prediction&diff=41657Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction2018-11-27T21:44:54Z<p>Aghabuss: </p>
<hr />
<div>The paper ''Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction'' was written by Roei Herzig* from Tel Aviv University, Moshiko Raboh* from Tel Aviv University, Gal Chechik from Google Brain, Bar-Ilan University, Jonathan Berant from Tel Aviv University, and Amir Globerson from Tel Aviv University. This paper is part of the NIPS 2018 conference to be hosted in December 2018 at Montréal, Canada. This paper summary is based on version 3 of the pre-print (as of May 2018) obtained from [https://arxiv.org/pdf/1802.05451v3.pdf arXiv] <br />
<br />
(*) Equal contribution<br />
<br />
=Motivation=<br />
In the field of artificial intelligence, a major goal is to enable machines to understand complex images, such as the underlying relationships between objects that exist in each scene. Although there are models today that capture both complex labels and interactions between labels, there is a disconnect for what guidelines should be used when leveraging deep learning. This paper introduces a design principle for such models that stem from the concept of permutation invariance and proves state of the art performance on models that follow this principle.<br />
<br />
The primary contributions that this paper makes include:<br />
# Deriving sufficient and necessary conditions for respecting graph-permutation invariance in deep structured prediction architectures<br />
# Empirically proving the benefit of graph-permutation invariance<br />
# Developing a state-of-the-art model for scene graph predictions over a large set of complex visual scenes<br />
<br />
=Introduction=<br />
In order to make a machine to interpret complex visual scenes, it must recognize and understand both objects and relationships between the objects in the scene. A '''scene graph''' is a representation of the set of objects and relations that exist in the scene, where objects are represented as nodes, relations are represented as edges connecting the different nodes. Hence, the prediction of the scene graph is analogous to inferring the joint set of objects and relations of a visual scene.<br />
<br />
[[File:scene_graph_example.png|600px|center]]<br />
<br />
Given that objects in scenes are interdependent on each other, joint prediction of the objects and relations is necessary. The field of structured prediction, which involves the general problem of inferring multiple inter-dependent labels, is of interest for this problem.<br />
<br />
In structured prediction models, a score function <math>s(x, y)</math> is defined to evaluate the compatibility between label <math>y</math> and input <math>x</math>. For instance, when interpreting the scene of an image, <math>x</math> refers to the image itself, and <math>y</math> refers to a complex label, which contains both the objects and the relations between objects. As with most other inference methods, the goal is to find the label <math>y^*</math> such that <math>s(x,y)</math> is maximized, <math> y^*=argmax_y s(x,y)</math>. However, the major concern is that the space for possible label assignments grows exponentially with respect to input size. For example, although an image may seem very simple, the corpus containing possible labels for objects may be very large, rendering it difficult to optimize the scoring function. <br />
<br />
The paper presents an alternative approach, for which input <math>x</math> is mapped to structured output <math>y</math> using a "black box" neural network, omitting the definition of a score function. The main concern for this approach is the determination of the network architecture.<br />
<br />
The model is evaluated by firstly demonstrating the importance of permutation invariance on a synthetic data set. The approach laid out by the authors is then shown to respect permutation invariance, and results are compared to a competitive benchmark. This method achieves state-of-the-art results.<br />
<br />
=Structured prediction=<br />
This paper further considers structured predictions using score-based methods. For structured predictions that follow a score-based approach, a score function <math>s(x, y)</math> is used to measure how compatible label <math>y</math> is for input <math>x</math> and is also used to infer a label by maximizing <math>s(x, y)</math>. To optimize the score function, previous works have decomposed <math>s(x,y) = \sum_i f_i(x,y)</math> in order to facilitate efficient optimization which is done by optimizing the local score function, <math>\max_y f_i(x,y)</math>, with a small subset of the <math>y</math> variables.<br />
<br />
Recently, modeling the <math>f_i </math> functions as deep networks is a new interest. In such area of structured predictions, the most commonly-used score functions include the singleton score function <math>f_i(y_i, x)</math> and pairwise score function <math>f_{ij} (y_i, y_j, x)</math>. Previous works explored a two-stage architectures (learn local scores independently of the structured prediction goal), end-to-end architectures (to include the inference algorithm within the computation graph), and modelling global factors. <br />
<br />
==Advantages of using score-based methods==<br />
# Allow for intuitive specification of local dependencies between labels, and how they map to global dependencies<br />
# Linear score functions offer natural convex surrogates<br />
# Inference in large label space is sometimes possible via exact algorithms or empirically accurate approximations<br />
<br />
The concern for modeling score functions using deep networks is that learning may no longer be convex. Hence, the paper presents properties for how deep networks can be used for structured predictions by considering architectures that do not require explicit maximization of a score function.<br />
<br />
=Background, Notations, and Definitions=<br />
We denote <math>y</math> as a structured label where <math>y = [y_1, \dots, y_n]</math><br />
<br />
'''Score functions:''' for score-based methods, the score is defined as either the sum of a set of singleton scores <math>f_i = f_i(y_i, x)</math> or the sum of pairwise scores <math>f_{ij} = f_{ij}(y_i, y_j, x)</math>.<br />
<br />
Let <math>s(x,y)</math> be the score of a score-based method. Then:<br />
<br />
<div align="center"><br />
<math>s(x,y) = \begin{cases}<br />
\sum_i f_i ~ \text{if we have a set of singleton scores}\\<br />
\sum_{ij} f_{ij} ~ \text{if we have a set of pairwise scores } \\<br />
\end{cases}</math><br />
</div><br />
<br />
'''Inference algorithm:''' an inference algorithm takes input set of local scores (either <math>f_i</math> or <math>f_{ij}</math>) and outputs an assignment of labels <math>y_1, \dots, y_n</math> that maximizes score function <math>s(x,y)</math><br />
<br />
'''Graph labeling function:''' a graph labeling function <math>\mathcal{F} : (V,E) \rightarrow Y</math> is a function that takes input of: an ordered set of node features <math>V = [z_1, \dots, z_n]</math> and an ordered set of edge features <math>E = [z_{1,2},\dots,z_{i,j},\dots,z_{n,n-1}]</math> to output set of node labels <math>\mathbf{y} = [y_1, \dots, y_n]</math>. For instance, <math>z_i</math> can be set equal to <math>f_i</math> and <math>z_{ij}</math> can be set equal to <math>f_{ij}</math>.<br />
<br />
For convenience, the joint set of nodes and edges will be denoted as <math>\mathbf{z}</math> to be a size <math>n^2</math> vector (<math>n</math> nodes and <math>n(n-1)</math> edges).<br />
<br />
'''Permutation:''' Let <math>z</math> be a set of node and edge features. Given a permutation <math>\sigma</math> of <math>\{1,\dots,n\}</math>, let <math>\sigma(z)</math> be a new set of node and edge features given by [<math>\sigma(z)]_i = z_{\sigma(i)}</math> and <math>[\sigma(z)]_{i,j} = z_{\sigma(i), \sigma(j)}</math><br />
<br />
'''One-hot representation:''' <math>\mathbf{1}[j]</math> be a one-hot vector with 1 in the <math>j^{th}</math> coordinate<br />
<br />
=Permutation-Invariant Structured prediction=<br />
<br />
With permutation-invariant structured prediction, we would expect the algorithm to produce the same result given the same score function. For instance, consider the case where we have label space for 3 variables <math>y_1, y_2, y_3</math> with input <math>\mathbf{z} = (f_1, f_2, f_3, f_{12}, f_{13}, f_{23})</math> that outputs label <math>\mathbf{y} = (y_1^*, y_2^*, y_3^*)</math>. Then if the algorithm is run on a permuted version input <math>z' = (f_2, f_1, f_3, f_{21}, f_{23}, f_{13})</math>, we would expect <math>\mathbf{y} = (y_2^*, y_1^*, y_3^*)</math> given the same score function.<br />
<br />
'''Graph permutation invariance (GPI):''' a graph labeling function <math>\mathcal{F}</math> is graph-permutation invariant, if for all permutations <math>\sigma</math> of <math>\{1, \dots, n\}</math> and for all nodes <math>z</math>, <math>\mathcal{F}(\sigma(\mathbf{z})) = \sigma(\mathcal{F}(\mathbf{z}))</math>. Practically speaking, graph permutation means that the same graph is constructed, no matter the order in which elements are predicted. In scene graph generation approaches, Region Proposal Networks are often used as an initial pre-processing step. The results from these (cropped images representing bounding boxes) are then sequentially fed through a respective vertex (or edge) detection network. The idea behind Permutation Invariance is that no matter the order these are passed in, the final scene graph is identical. In effect, this means not connecting vertices that should not be connected simply because a more promising vertex has not yet been identified. <br />
<br />
The paper presents a theorem on the necessary and sufficient conditions for a function <math>\mathcal{F}</math> to be graph permutation invariant. Intuitively, because <math>\mathcal{F}</math> is a function that takes an ordered set <math>z</math> as input, the output on <math>\mathbf{z}</math> could very well be different from <math>\sigma(\mathbf{z})</math>, which means <math>\mathcal{F}</math> needs to have some sort of symmetry in order to sustain <math>[\mathcal{F}(\sigma(\mathbf{z}))]]_k = [\mathcal{F}(\mathbf{z})]_{\sigma(k)}</math>.<br />
<br />
[[File:graph_permutation_invariance.jpg|400px|center]]<br />
<br />
==Theorem 1==<br />
Let <math>\mathcal{F}</math> be a graph labeling function. Then <math>\mathcal{F}</math> is graph-permutation invariant if and only if there exist functions <math>\alpha, \rho, \phi</math> such that for all <math>k=1, .., n</math>:<br />
\begin{align}<br />
[\mathcal{F}(\mathbf{z})]_k = \rho(\mathbf{z}_k, \sum_{i=1}^n \alpha(\mathbf{z}_i, \sum_{i\neq j} \phi(\mathbf{z}_i, \mathbf{z}_{i,j}, \mathbf{z}_j)))<br />
\end{align}<br />
where <math>\phi: \mathbb{R}^{2d+e} \rightarrow \mathbb{R}^L, \alpha: \mathbb{R}^{d + L} \rightarrow \mathbb{R}^{W}, p: \mathbb{R}^{W+d} \rightarrow \mathbb{R}</math>.<br />
<br />
Notice that for the dimensions of inputs and outputs, <math>d</math> refers to the number of singleton features in <math>z</math> and <math>e</math> refers to the number of edges. <br />
<br />
[[File:GPI_architecture.jpg|thumb|A schematic representation of the GPI architecture. Singleton features <math>z_i</math> are omitted for simplicity. First, the features <math>z_{i,j}</math> are processed element-wise by <math>\phi</math>. Next, they are summed to create a vector <math>s_i</math>, which is concatenated with <math>z_i</math>. Third, a representation of the entire graph is created by applying <math>\alpha\ n</math> times and summing the created vector. The graph representation is then finally processed by <math>\rho</math> together with <math>z_k</math>.|600px|center]]<br />
<br />
==Proof Sketch for Theorem 1==<br />
The proof of this theorem can be found in the paper. A proof sketch is provided below:<br />
<br />
'''For the forward direction''' (function that follows the form set out in equation (1) is GPI):<br />
# Using definition of permutation <math>\sigma</math>, and rewriting <math>[F(z)]_{\sigma(k)}</math> in the form from equation (1)<br />
# Second argument of <math>\rho</math> is invariant under <math>\sigma</math>, since it takes the sum of all indices <math>i</math> and all other indices <math>j \neq i </math>.<br />
<br />
'''For the backward direction''' (any black-box GPI function can be expressed in the form of equation 1):<br />
# Construct <math>\phi, \alpha</math> such that second argument of <math>\rho</math> contains all information about graph features of <math>z</math>, including edges that the features originate from<br />
# Assume each <math>z_k</math> uniquely identifies the node and <math>\mathcal{F}</math> is a function only of pairwise features <math>z_{i,j}</math><br />
# Construct <math>H</math> be a perfect hash function with <math>L</math> buckets, and <math>\phi</math> which maps '''pairwise features''' to a vector of size <math>L</math><br />
# <math>*</math>Construct <math>\phi(z_i, z_{i,j}, z_j) = \mathbf{1}[H(z_j)] z_{i,j}</math>, which intuitively means that <math>\phi</math> stores <math>z_{i,j}</math> in the unique bucket for node <math>j</math><br />
# Construct function <math>\alpha</math> to output a matrix <math>\mathbb{R}^{L \times L}</math> that maps each pairwise feature into unique positions (<math>\alpha(z_i, s_i) = \mathbf{1}[H(z_i)]s_i^T</math>)<br />
# Construct matrix <math>M = \sum_i \alpha(z_i,s_i)</math> by discarding rows/columns in <math>M</math> that do not correspond to original nodes (which reduces dimension to <math>n\times n</math>; set <math>\rho</math> to have same outcome as <math>\mathcal{F}</math>, and set the output of <math>\mathcal{F}</math> on <math>M</math> to be the labels <math>\mathbf{y} = y_1, \dots, y_n</math><br />
<br />
<math>*</math>The paper presents the proof for the edge features <math>z_{ij}</math> being scalar (<math>e = 1</math>) for simplicity, which can be extended easily to vectors with additional indexing.<br />
<br />
Although the results discussed previously apply to complete graphs (edges apply to all feature pairs), it can be easily extended to incomplete graphs. For incomplete graphs, the input to F only contains the features corresponding to valid edges of the graph. The authors are only interested in invariances that preserve the graph structure. Thus, in place of permutation-invariance, it is now an automorphism-invariance.<br />
<br />
==Implications and Applications of Theorem 1==<br />
===Key Implications of Theorem 1===<br />
# Architecture "collects" information from the different edges of the graph, and does so in an invariant fashion using <math>\alpha</math> and <math>\phi</math><br />
# Architecture is parallelizable, since all <math>\phi</math> functions can be applied simultaneously. In contrast, recurrent models (Zellers et al. 2017) are harder to parallelize and are thus practically slower.<br />
<br />
===Some applications of Theorem 1===<br />
# '''Attention:''' the concept of attention can be implemented in the GPI characterization, with slight alterations to the functions <math>\alpha</math> and <math>\phi</math>. In attention each node aggregates features of neighbors through a function of neighbor's relevance. Which means the label of an entity could depend strongly on its close entity. The complete details can be found in the supplementary materials of the paper.<br />
<br />
# '''RNN:''' recurrent architectures can maintain GPI property, since all GPI function <math>\mathcal{F}</math> are closed under composition. The output of one step after running <math>\mathcal{F}</math> will act as input for the next step, but maintain the GPI property throughout.<br />
<br />
=Related Work=<br />
# '''Architectural invariance:''' suggested recently in a 2017 paper called Deep Sets by Zaheer et al., which considers the case of invariance that is more restrictive.<br />
# '''Deep structured prediction:''' previous work applied deep learning to structured prediction, for instance, semantic segmentation. Some algorithms include message passing algorithms, gradient descent for maximizing score functions, greedy decoding (inference of labels based on time of previous labels). For example, Xu et al. 2017 propose a novel end-to-end model that generates structured scene representation, and their model solves the scene graph inference problem using standard RNNs and learns to iteratively improves its predictions via message passing. Apart from those algorithms, deep learning has been applied to other graph-based problems such as the Travelling Salesman Problem (Bello et al., 2016; Gilmer et al., 2017; Khalil et al., 2017). However, none of the previous work specifically address the notion of invariance in the general architecture, but rather focus on message passing architectures that can be generalized by this paper.<br />
# '''Scene graph prediction:''' scene graph extraction allows for reasoning, question answering, and image retrieval (Johnson et al., 2015; Lu et al., 2016; Raposo et al., 2017). Some other works in this area include object detection, action recognition, and even the detection of human-object interactions (Liao et al., 2016; Plummer et al., 2017). Additional work has been done with the use of message passing algorithms (Xu et al., 2017), word embeddings (Lu et al., 2016), and end-to-end prediction directly from pixels (Newell & Deng, 2017). A notable mention is NeuralMotif (Zellers et al., 2017), which the authors describe as the current state-of-the-art model for scene graph predictions on Visual Genome dataset. It uses an RNN that supplies global context by reading the independent predictions sequentially for each entity and relation and then conducts further refinement on the predictions. The NeuralMotif model has a fixed order in which the RNN reads its inputs and thereby maintains GPI. However, this fixed order is not guaranteed to be optimal.<br />
# '''Burst Image Deblurring Using Permutation Invariant Convolutional Neural Networks:''' similar ideas were applied, where Permutation Invariant CNN, are used to restore sharp and noise-free images from bursts of photographs affected by hand tremor and noise. This presented good quality images with lots of details for challenging datasets.<br />
<br />
=Experimental Results=<br />
<br />
The authors evaluated the advantage of GPI architectures empirically. They first utilized synthetic graph labeling and then used scene-graph classification for mapping images.<br />
<br />
==Synthetic Graph Labeling==<br />
The authors created a synthetic problem to study GPI. This involved using an input graph <math>G = (V,E)</math> where each node <math>i</math> belongs to the set <math>\Gamma(i) \in \{1, \dots, K\}</math> where <math>K</math> is the number of samples. The task is to compute for each node, the number of neighbours that belong to the same set (i.e. finding the label of the node <math>i</math> if <math>y_i = \sum_{j \in N(i)} \mathbf{1}[\Gamma(i) = \Gamma(j)]</math>) . Then, random graphs (each with 10 nodes) were generated by sampling edges, and the set <math>\Gamma(i) \in \{1, \dots, K\}</math>for each node independently and uniformly.<br />
The node features of the graph <math>z_i \in \{0,1\}^K</math> are one-hot vectors of <math>\Gamma(i)</math>, and each pairwise edge feature <math>z_{ij} \in \{0, 1\}</math> denote whether the edge <math>ij</math> is in the edge set <math>E</math>. <br />
3 architectures were studied in this paper:<br />
# '''GPI-architecture for graph prediction''' (without attention and RNN)<br />
# '''LSTM''': replacing <math>\sum \phi(\cdot)</math> and <math>\sum \alpha(\cdot)</math> in the form of Theorem 1 using two LSTMs with state size 200, reading their input in random order<br />
# '''Fully connected feed-forward network''': with 2 hidden layers, each layer containing 1,000 nodes; the input is a concatenation of all nodes and pairwise features, and the output is all node predictions<br />
<br />
The results show that the GPI architecture requires far fewer samples to converge to the correct solution.<br />
[[File:GPI_synthetic_example.jpg|450px|center]]<br />
<br />
This experimental result is meant to demonstrate sample complexity. For fairness, all three models were constructed with a similar number of trainable parameters. The results tie back in with the author's comment that a black-box model which violates permutation invariant structure wastes capacity on learning it at training time. This illustrates the advantage of an architecture with a proper inductive bias.<br />
<br />
==Scene-Graph Classification==<br />
Applying the concept of GPI to Scene-Graph Prediction (SGP) is the main task of this paper. The input to this problem is an image, along with a set of annotated bounding boxes for the entities in the image. The goal is to correctly label each entity within the bounding boxes and the relationship between every pair of entities, resulting in a coherent scene graph.<br />
<br />
The authors describe two different types of variables to predict. The first type is entity variables <math>[y_1, \dots, y_n]</math> for all bounding boxes, where each <math>y_i</math> can take one of L values and refers to objects such as "dog" or "man". The second type is relation variables <math>[y_{n+1}, \cdots, y_{n^2}]</math>, where each <math>y_i</math> represents the relation (e.g. "on", "below") between a pair of bounding boxes (entities).<br />
<br />
The scene graph and contain two types of edges:<br />
# '''Entity-entity edge''': connecting two entities <math>y_i</math> and <math>y_j</math> for <math>1 \leq i \neq j \leq n</math><br />
# '''Entity-relation edges''': connecting every relation variable <math>y_k</math> for <math>k > n</math> to two entities<br />
<br />
The feature set <math>\mathbf{z}</math> is based on the baseline model from Zellers et al. (2017). For entity variables <math>y_i</math>, the vector <math>\mathbf{z}_i \in \mathbb{R}^L</math> models the probability of the entity appearing in <math>y_i</math>. <math>\mathbf{z}_i</math> is augmented by the coordinates of the bounding box. Similarly for relation variables <math>y_j</math>, the vector <math>\mathbf{z}_j \in \mathbb{R}^R</math>, models the probability of the relations between the two entities in <math>j</math>. For entity-entity pairwise features <math>\mathbf{z}_{i,j}</math>, there is a similar representation of the probabilities for the pair. The SGP outputs probability distributions over all entities and relations, which will then be used as input recurrently to maintain GPI. Finally, word embeddings are used and concatenated for the most probable entity-relation labels.<br />
<br />
'''Components of the GPI architecture''' (ent for entity, rel for relation)<br />
# <math>\phi_{ent}</math>: network that integrates two entity variables <math>y_i</math> and <math>y_j</math>, with input <math>z_i, z_j, z_{i,j}</math> and output vector of <math>\mathbb{R}^{n_1}</math> <br />
# <math>\alpha_{ent}</math>: network with inputs from <math>\phi_{ent}</math> for all neighbours of an entity, and uses attention mechanism to output vector <math>\mathbb{R}^{n_2}</math> <br />
# <math>\rho_{ent}</math>: network with inputs from the various <math>\mathbb{R}^{n_2}</math> vectors, and outputs <math>L</math> logits to predict entity value<br />
# <math>\rho_{rel}</math>: network with inputs <math>\alpha_{ent}</math> of two entities and <math>z_{i,j}</math>, and output into <math>R</math> logits<br />
<br />
==Set-up and Results==<br />
'''Dataset''': based on Visual Genome (VG) by (Krishna et al., 2017), which contains a total of 108,077 images annotated with bounding boxes, entities, and relations. An average of 12 entities and 7 relations exist per image. For a fair comparison with previous works, data from (Xu et al., 2017) for train and test splits were used. The authors used the same 150 entities and 50 relations as in (Xu et al., 2017; Newell & Deng, 2017; Zellers et al., 2017). Hyperparameters were tuned using a 70K/5K/32K split for training, validation, and testing respectively.<br />
<br />
'''Training''': all networks were trained using the Adam optimizer, with a batch size of 20. The loss function was the sum of cross-entropy losses over all of entities and relations. Penalties for misclassified entities were 4 times stronger than that of relations. Penalties for misclassified negative relations were 10 times weaker than that of positive relations.<br />
<br />
'''Evaluation''': there are three major tasks when inferring from the scene graph. The authors focus on the following:<br />
# '''SGCIs''': given ground-truth entity bounding boxes, predict all entity and relations categories<br />
# '''PredCIs''': given annotated bounding boxes with entity labels, predict all relations<br />
<br />
The evaluation metric Recall@K (shortened to R@K) is drawn from (Lu et al., 2016). This metric is the fraction of correct ground-truth triplets that appear within the <math>K</math> most confident triplets predicted by the model. Graph-constrained protocol requires the top-<math>K</math> triplets to assign one consistent class per entity and relation. The unconstrained protocol does not enforce such constraint.<br />
<br />
'''Models and baselines''': The authors compared variants of the GPI approach against four baselines, state-of-the-art models on completing scene graph sub-tasks. To maintain consistency, all models used the same training/testing data split, in addition to the preprocessing as per (Xu et al., 2017).<br />
<br />
'''Baselines from existing state-of-the-art models'''<br />
# (Lu et al., 2016): use of word embeddings to fine-tune the likelihood of predicted relations<br />
# (Xu et al., 2017): message passing algorithm between entities and relations to iteratively improve feature map for prediction<br />
# (Newell & Deng, 2017): Pixel2Graph, uses associative embeddings to produce a full graph from image<br />
# (Zellers et al., 2017): NeuralMotif method, encodes global context to capture higher-order motif in scene graphs; Baseline outputs entities and relations distributions without using global context<br />
<br />
'''GPI models'''<br />
# '''GPI with no attention mechanism''': simply following Theorem 1's functional form, with summation over features<br />
# '''GPI NeighborAttention''': same GPI model, but considers attention over neighbours features<br />
# '''GPI Linguistic''': similar to NeighborAttention model, but concatenates word embedding vectors<br />
<br />
'''Key Results''': The GPI Linguistic approach outperforms all baseline for SGCIs, and has similar performance to the state of the art NeuralMotifs method. The authors argue that PredCI is an easier task with less structure, yielding high performance for the existing state of the art models.<br />
<br />
[[File:GPI_table_results.png|700px|center]]<br />
<br />
=Conclusion=<br />
<br />
A deep learning approach was presented in this paper to structured prediction, which constrains the architecture to be invariant to structurally identical inputs. This approach relies on pairwise features which are capable of describing inter-label correlations and inherits the intuitive aspect of score-based approaches. The output produced is invariant to equivalent representation of the pairwise terms. <br />
<br />
As future work, the axiomatic approach can be extended; for example in image labeling, geometric variances such as shifts or rotations may be desired (or in other cases invariance to feature permutations may be desired). Additionally, exploring algorithms that discover symmetries for deep structured prediction when the invariant structure is unknown and should be discovered from data is also an interesting extension of this work.<br />
<br />
=Critique=<br />
The paper's contribution comes from the novelty of the permutation invariance as a design guideline for structured prediction. Although not explicitly considered in many of the previous works, the idea of invariance in architecture has already been considered in Deep Sets by (Zaheer et al., 2017). This paper characterizes relaxes the condition on the invariance as compared to that of previous works. In the evaluation of the benefit of GPI models, the paper used a synthetic problem to illustrate the fact that far fewer samples are required for the GPI model to converge to 100% accuracy. However, when comparing the true task of scene graph prediction against the state-of-the-art baselines, the GPI variants had only marginal higher Recall@K scores. The true benefit of this paper's discovery is the avoidance of maximizing a score function (leading computationally difficult problem), and instead directly producing output invariant to how we represent the pairwise terms.<br />
<br />
=References=<br />
<br />
[Lu et al., 2016] Lu, Cewu, Krishna, Ranjay, Bernstein, Michael S., and Li, Fei-Fei. Visual relationship detection with<br />
language priors. In European Conf. Comput. Vision, pp. 852–869, 2016.<br />
<br />
Roei Herzig, Moshiko Raboh, Gal Chechik, Jonathan Berant, Amir Globerson, Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction, 2018.<br />
<br />
Additional resources from Moshiko Raboh's [https://github.com/shikorab/SceneGraph GitHub]</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=conditional_neural_process&diff=41656conditional neural process2018-11-27T21:42:47Z<p>Aghabuss: </p>
<hr />
<div>== Motivation ==<br />
<br />
Deep neural networks are good at function approximations, yet they are typically trained from scratch for each new function. While Bayesian methods, such as Gaussian Processes (GPs), exploit prior knowledge to quickly infer the shape of a new function at test time. Yet GPs<br />
are computationally expensive, and it can be hard to design appropriate priors. Hence the authors propose a propose a family of neural models called, Conditional Neural Processes (CNPs), that combine the benefits of both. <br />
<br />
== Introduction ==<br />
<br />
To train a model effectively, deep neural networks typically require large datasets. To mitigate this data efficiency problem, learning in two phases is one approach: the first phase learns the statistics of a generic domain without committing to a specific learning task; the second phase learns a function for a specific task but does so using only a small number of data points by exploiting the domain-wide statistics already learned. Taking a probabilistic stance and specifying a distribution over functions (stochastic processes) is another approach -- Gaussian Processes being a commonly used example of this. Such Bayesian methods can be computationally expensive. <br />
<br />
The authors of the paper propose a family of models that represent solutions to the supervised problem, and an end-to-end training approach to learning them that combines neural networks with features reminiscent of Gaussian Processes. They call this family of models Conditional Neural Processes (CNPs). CNPs can be trained on very few data points to make accurate predictions, while they also have the capacity to scale to complex functions and large datasets.<br />
<br />
== Model ==<br />
Consider a data set <math display="inline"> \{x_i, y_i\} </math> with evaluations <math display="inline">y_i = f(x_i) </math> for some unknown function <math display="inline">f</math>. Assume <math display="inline">g</math> is an approximating function of f. The aim is to minimize the loss between <math display="inline">f</math> and <math display="inline">g</math> on the entire space <math display="inline">X</math>. In practice, the routine is evaluated on a finite set of observations.<br />
<br />
<br />
Let training set be <math display="inline"> O = \{x_i, y_i\}_{i = 0} ^{n-1}</math>, and test set be <math display="inline"> T = \{x_i, y_i\}_{i = n} ^ {n + m - 1} \subset X</math> of unlabelled points.<br />
<br />
P be a probability distribution over functions <math display="inline"> F : X \to Y</math>, formally known as a stochastic process. Thus, P defines a joint distribution over the random variables <math display="inline"> {f(x_i)}_{i = 0} ^{n + m - 1}</math>. Therefore, for <math display="inline"> P(f(x)|O, T)</math>, our task is to predict the output values <math display="inline">f(x_i)</math> for <math display="inline"> x_i \in T</math>, given <math display="inline"> O</math>. <br />
<br />
A common assumption made on P is that all function evaluations of <math display="inline"> f </math> is Gaussian distributed. The random functions class is called Gaussian Processes (GPs). This framework of the stochastic process allows a model to be data efficient, however, it's hard to get appropriate priors and stochastic processes are expensive in computation, scaling poorly with <math>n</math> and <math>m</math>. One of the examples is GPs, which has running time <math>O(n+3)^3</math>.<br />
<br />
[[File:001.jpg|300px|center]]<br />
<br />
== Conditional Neural Process ==<br />
<br />
Conditional Neural Process models directly parametrize conditional stochastic processes without imposing consistency with respect to some prior process. CNP parametrize distributions over <math display="inline">f(T)</math> given a distributed representation of <math display="inline">O</math> of fixed dimensionality. Thus, the mathematical guarantees associated with stochastic processes is traded off for functional flexibility and scalability.<br />
<br />
CNP is a conditional stochastic process <math display="inline">Q_\theta</math> defines distributions over <math display="inline">f(x_i)</math> for <math display="inline">x_i \in T</math>, given a set of observations <math display="inline">O</math>. For stochastic processs, the authors assume that <math display="inline">Q_{\theta}</math> is invariant to permutations, and <math display="inline">Q_\theta(f(T) | O, T)= Q_\theta(f(T') | O, T')=Q_\theta(f(T) | O', T) </math> when <math> O', T'</math> are permutations of <math display="inline">O</math> and <math display="inline">T </math>. In this work, we generally enforce permutation invariance with respect to <math display="inline">T</math> be assuming a factored structure, which is the easiest way to ensure a valid stochastic process. That is, <math display="inline">Q_\theta(f(T) | O, T) = \prod _{x \in T} Q_\theta(f(x) | O, x)</math>. Moreover, this framework can be extended to non-factored distributions.<br />
<br />
In detail, the following architecture is used<br />
<br />
<math display="inline">r_i = h_\theta(x_i, y_i)</math> for any <math display="inline">(x_i, y_i) \in O</math>, where <math display="inline">h_\theta : X \times Y \to \mathbb{R} ^ d</math><br />
<br />
<math display="inline">r = r_i * r_2 * ... * r_n</math>, where <math display="inline">*</math> is a commutative operation that takes elements in <math display="inline">\mathbb{R}^d</math> and maps them into a single element of <math display="inline">\mathbb{R} ^ d</math><br />
<br />
<math display="inline">\Phi_i = g_\theta</math> for any <math display="inline">x_i \in T</math>, where <math display="inline">g_\theta : X \times \mathbb{R} ^ d \to \mathbb{R} ^ e</math> and <math display="inline">\Phi_i</math> are parameters for <math display="inline">Q_\theta</math><br />
<br />
Note that this architecture ensures permutation invariance and <math display="inline">O(n + m)</math> scaling for conditional prediction. Also, <math display="inline">r = r_i * r_2 * ... * r_n</math> can be computed in <math display="inline">O(n)</math>, this architecture supports streaming observation with minimal overhead.<br />
<br />
We train <math display="inline">Q_\theta</math> by asking it to predict <math display="inline">O</math> conditioned on a randomly<br />
chosen subset of <math display="inline">O</math>. This gives the model a signal of the uncertainty over the space X inherent in the distribution<br />
P given a set of observations. The authors let <math display="inline"> f \sim P</math>, <math display="inline"> O = \{(x_i, y_i)\}_{i = 0} ^{n-1}</math>, and N ~ uniform[0, 1, ..... ,n-1]. Subset <math display="inline"> O = \{(x_i, y_i)\}_{i = 0} ^{N}</math> that is first N elements of <math display="inline">O</math> is regarded as condition. The negative conditional log probability is given by<br />
\[\mathcal{L}(\theta)=-\mathbb{E}_{f \sim p}[\mathbb{E}_{N}[\log Q_\theta(\{y_i\}_{i = 0} ^{n-1}|O_{N}, \{x_i\}_{i = 0} ^{n-1})]]\]<br />
Thus, the targets it scores <math display="inline">Q_\theta</math> on include both the observed <br />
and unobserved values. In practice, Monte Carlo estimates of the gradient of this loss is taken by sampling <math display="inline">f</math> and <math display="inline">N</math>. <br />
<br />
This approach shifts the burden of imposing prior knowledge from an analytic prior to empirical data. This has the advantage of liberating a practitioner from having to specify an analytic form for the prior, which is ultimately<br />
intended to summarize their empirical experience. Still, we emphasize that the <math display="inline">Q_\theta</math> are not necessarily a consistent set of conditionals for all observation sets, and the training routine does not guarantee that.<br />
<br />
In summary,<br />
<br />
1. A CNP is a conditional distribution over functions<br />
trained to model the empirical conditional distributions<br />
of functions <math display="inline">f \sim P</math>.<br />
<br />
2. A CNP is permutation invariant in <math display="inline">O</math> and <math display="inline">T</math>.<br />
<br />
3. A CNP is scalable, achieving a running time complexity<br />
of <math display="inline">O(n + m)</math> for making <math display="inline">m</math> predictions with <math display="inline">n</math><br />
observations.<br />
<br />
== Related Work ==<br />
<br />
===Gaussian Process Framework===<br />
<br />
A Gaussian Process (GP) is a non-parametric method for regression, used extensively for regression and classification problems in the machine learning community. A GP is defined as a collection of random variables, any finite number of which have a joint Gaussian distribution.<br />
A standard approach is to model data as <math>y = m(X, φ) + \epsilon</math><br />
where m is the mean function with parameter vector <math>φ</math>, and <math>\epsilon</math> represents independent and identically distributed (i.i.d.) Gaussian noise: <math>N\sim (0,\sigma^2)</math><br />
<br />
For more info on Gaussian Process Framework:<br />
[https://arxiv.org/abs/1506.07304 A Gaussian process framework for modeling instrumental systematics: application to transmission spectroscopy]<br />
<br />
Several papers attempt to address various issues with GPs. These include:<br />
* Using sparse GPs to aid in scaling (Snelson & Ghahramani, 2006)<br />
* Using Deep GPs to achieve more expressiveness (Damianou & Lawrence, 2013; Salimbeni & Deisenroth, 2017)<br />
* Using neural networks to learn more expressive kernels (Wilson et al., 2016)<br />
<br />
A Python resource for Gaussian Process Framework implementation: [https://github.com/SheffieldML/GPyimplementation Gaussian Process Framework in Python]<br />
<br />
<br />
The goal of this paper is to incorporate ideas from standard neural networks with Gaussian processes in order to overcome drawbacks of both. Bayesian techniques work better with less data, but complex Bayesian networks become intractable on even moderate sized data sizes. NNs on the other hand, cannot make use of prior knowledge and often have to be retrained from scratch. Without sufficient data, they also perform poorly. Combining both frameworks, we get Conditional Neural Processes serves to learn the kernels of the Gaussian Process through neural networks and uses these learned kernels on a framework similar to GPs for prediction.<br />
<br />
===Meta Learning===<br />
<br />
Meta-Learning attempts to allow neural networks to learn more generalizable functions, as opposed to only approximating one function. This can be done by learning deep generative models which can do few-shot estimations of data. This can be implemented with attention mechanisms or additional memory.<br />
<br />
Classification is another common task in meta-learning, few-shot classification algorithms usually rely on some distance metric in feature space to compare target images and the observations. Matching networks(Vinyals et al., 2016; Bartunov & Vetrov, 2016) are closely related to CNPs.<br />
<br />
Finally, the latest variant of Conditional Neural Process can also be seen as an approximated amortized version of Bayesian DL(Gal & Ghahramani, 2016; Blundell et al., 2015; Louizos et al., 2017; Louizos & Welling, 2017). For example, Gal & Ghahramani 2016 develop a new theoretical framework casting dropout training in deep neural networks as approximate Bayesian inference in deep Gaussian processes. Their theory extracts information from existing models and gives us tools to model uncertainty.<br />
<br />
== Experimental Result I: Function Regression ==<br />
<br />
Classical 1D regression task that used as a common baseline for GP is the first example. <br />
They generated two different datasets that consisted of functions<br />
generated from a GP with an exponential kernel. In the first dataset they used a kernel with fixed parameters, and in the second dataset, the function switched at some random point. on the real line between two functions, each sampled with<br />
different kernel parameters. At every training step, they sampled a curve from the GP, select<br />
a subset of n points as observations, and a subset of t points as target points. Using the model, the observed points are encoded using a three-layer MLP encoder h with a 128-dimensional output representation. The representations are aggregated into a single representation<br />
<math display="inline">r = \frac{1}{n} \sum r_i</math><br />
, which is concatenated to <math display="inline">x_t</math> and passed to a decoder g consisting of a five layer<br />
MLP. The function outputs a Gaussian mean and variance for the target outputs. The model is trained to maximize the log-likelihood of the target points using the Adam optimizer. <br />
<br />
Two examples of the regression results obtained for each<br />
of the datasets are shown in the following figure.<br />
<br />
[[File:007.jpg|300px|center]]<br />
<br />
They compared the model to the predictions generated by a GP with the correct<br />
hyperparameters, which constitutes an upper bound on our<br />
performance. Although the prediction generated by the GP<br />
is smoother than the CNP's prediction both for the mean<br />
and variance, the model is able to learn to regress from a few<br />
context points for both the fixed kernels and switching kernels.<br />
As the number of context points grows, the accuracy<br />
of the model improves and the approximated uncertainty<br />
of the model decreases. Crucially, we see the model learns<br />
to estimate its own uncertainty given the observations very<br />
accurately. Nonetheless, it provides a good approximation<br />
that increases in accuracy as the number of context points<br />
increases.<br />
Furthermore, the model achieves similarly good performance<br />
on the switching kernel task. This type of regression task<br />
is not trivial for GPs whereas in our case we only have to<br />
change the dataset used for training<br />
<br />
== Experimental Result II: Image Completion for Digits ==<br />
<br />
[[File:002.jpg|600px|center]]<br />
<br />
They also tested CNP on the MNIST dataset and use the test<br />
set to evaluate its performance. As shown in the above figure the<br />
model learns to make good predictions of the underlying<br />
digit even for a small number of context points. Crucially,<br />
when conditioned only on one non-informative context point the model’s prediction corresponds<br />
to the average overall MNIST digits. As the number<br />
of context points increases the predictions become more<br />
similar to the underlying ground truth. This demonstrates<br />
the model’s capacity to extract dataset specific prior knowledge.<br />
It is worth mentioning that even with a complete set<br />
of observations, the model does not achieve pixel-perfect<br />
reconstruction, as we have a bottleneck at the representation<br />
level.<br />
Since this implementation of CNP returns factored outputs,<br />
the best prediction it can produce given limited context<br />
information is to average over all possible predictions that<br />
agree with the context. An alternative to this is to add<br />
latent variables in the model such that they can be sampled<br />
conditioned on the context to produce predictions with high<br />
probability in the data distribution. <br />
<br />
<br />
An important aspect of the model is its ability to estimate<br />
the uncertainty of the prediction. As shown in the bottom<br />
row of the above figure, as they added more observations, the variance<br />
shifts from being almost uniformly spread over the digit<br />
positions to being localized around areas that are specific<br />
to the underlying digit, specifically its edges. Being able to<br />
model the uncertainty given some context can be helpful for<br />
many tasks. One example is active exploration, where the<br />
model has a choice over where to observe.<br />
They tested this by<br />
comparing the predictions of CNP when the observations<br />
are chosen according to uncertainty, versus random pixels. This method is a very simple way of doing active<br />
exploration, but it already produces better prediction results<br />
then selecting the conditioning points at random.<br />
<br />
== Experimental Result III: Image Completion for Faces ==<br />
<br />
<br />
[[File:003.jpg|400px|center]]<br />
<br />
<br />
They also applied CNP to CelebA, a dataset of images of<br />
celebrity faces and reported performance obtained on the<br />
test set.<br />
<br />
As shown in the above figure our model is able to capture<br />
the complex shapes and colors of this dataset with predictions<br />
conditioned on less than 10% of the pixels being<br />
already close to the ground truth. As before, given a few contexts<br />
points the model averages over all possible faces, but as<br />
the number of context pairs increases the predictions capture<br />
image-specific details like face orientation and facial<br />
expression. Furthermore, as the number of context points<br />
increases the variance is shifted towards the edges in the<br />
image.<br />
<br />
[[File:004.jpg|400px|center]]<br />
<br />
An important aspect of CNPs demonstrated in the above figure is<br />
it's flexibility not only in the number of observations and<br />
targets it receives but also with regards to their input values.<br />
It is interesting to compare this property to GPs on one hand,<br />
and to trained generative models (van den Oord et al., 2016;<br />
Gregor et al., 2015) on the other hand.<br />
The first type of flexibility can be seen when conditioning on<br />
subsets that the model has not encountered during training.<br />
Consider conditioning the model on one half of the image,<br />
fox example. This forces the model to not only predict the pixel<br />
values according to some stationary smoothness property of<br />
the images, but also according to global spatial properties,<br />
e.g. symmetry and the relative location of different parts of<br />
faces. As seen in the first row of the figure, CNPs are able to<br />
capture those properties. A GP with a stationary kernel cannot<br />
capture this, and in the absence of observations would<br />
revert to its mean (the mean itself can be non-stationary but<br />
usually, this would not be enough to capture the interesting<br />
properties).<br />
<br />
In addition, the model is flexible with regards to the target<br />
input values. This means, e.g., we can query the model<br />
at resolutions it has not seen during training. We take a<br />
model that has only been trained using pixel coordinates of<br />
a specific resolution and predict at test time subpixel values<br />
for targets between the original coordinates. As shown in<br />
Figure 5, with one forward pass we can query the model at<br />
different resolutions. While GPs also exhibit this type of<br />
flexibility, it is not the case for trained generative models,<br />
which can only predict values for the pixel coordinates on<br />
which they were trained. In this sense, CNPs capture the best<br />
of both worlds – it is flexible in regards to the conditioning<br />
and prediction task and has the capacity to extract domain<br />
knowledge from a training set.<br />
<br />
[[File:010.jpg|400px|center]]<br />
<br />
<br />
They compared CNPs quantitatively to two related models:<br />
kNNs and GPs. As shown in the above table CNPs outperform<br />
the latter when a number of context points are small (empirically<br />
when half of the image or less is provided as context).<br />
When the majority of the image is given as context exact<br />
methods like GPs and kNN will perform better. From the table<br />
we can also see that the order in which the context points<br />
are provided is less important for CNPs, since providing the<br />
context points in order from top to bottom still results in<br />
good performance. Both insights point to the fact that CNPs<br />
learn a data-specific ‘prior’ that will generate good samples<br />
even when the number of context points is very small.<br />
<br />
== Experimental Result IV: Classification ==<br />
Finally, they applied the model to one-shot classification using the Omniglot dataset. This dataset consists of 1,623 classes of characters from 50 different alphabets. Each class has only 20 examples and as such this dataset is particularly suitable for few-shot learning algorithms. The authors used 1,200 randomly selected classes as their training set and the remainder as the testing data set.<br />
<br />
Additionally, to apply data augmentation the authors cropped the image from 32 × 32 to 28 × 28, applied small random<br />
translations and rotations to the inputs, and also increased<br />
the number of classes by rotating every character by 90<br />
degrees and defining that to be a new class. They generated<br />
the labels for an N-way classification task by choosing N<br />
random classes at each training step and arbitrarily assigning<br />
the labels 0, ..., N − 1 to each.<br />
<br />
<br />
[[File:008.jpg|400px|center]]<br />
<br />
Given that the input points are images, they modified the architecture<br />
of the encoder h to include convolution layers as<br />
mentioned in section 2. In addition, they only aggregated over<br />
inputs of the same class by using the information provided<br />
by the input label. The aggregated class-specific representations<br />
are then concatenated to form the final representation.<br />
Given that both the size of the class-specific representations<br />
and the number of classes is constant, the size of the final<br />
representation is still constant and thus the O(n + m)<br />
runtime still holds.<br />
The results of the classification are summarized in the following table<br />
CNPs achieve higher accuracy than models that are significantly<br />
more complex (like MANN). While CNPs do not<br />
beat state of the art for one-shot classification our accuracy<br />
values are comparable. Crucially, they reached those values<br />
using a significantly simpler architecture (three convolutional<br />
layers for the encoder and a three-layer MLP for the<br />
decoder) and with a lower runtime of O(n + m) at test time<br />
as opposed to O(nm)<br />
<br />
== Conclusion ==<br />
<br />
The paper introduced Conditional Neural Processes,<br />
a model that is both flexible at test time and has the<br />
capacity to extract prior knowledge from training data.<br />
<br />
We had demonstrated its ability to perform a variety of tasks<br />
including regression, classification and image completion.<br />
The paper compared CNP's to Gaussian Processes on one hand, and<br />
deep learning methods on the other, and also discussed the<br />
relation to meta-learning and few-shot learning.<br />
It is important to note that the specific CNP implementations<br />
described here are just simple proofs-of-concept and can<br />
be substantially extended, e.g. by including more elaborate<br />
architectures in line with modern deep learning advances.<br />
To summarize, this work can be seen as a step towards learning<br />
high-level abstractions, one of the grand challenges of<br />
contemporary machine learning. Functions learned by most<br />
Conditional Neural Processes<br />
conventional deep learning models are tied to a specific, constrained<br />
statistical context at any stage of training. A trained<br />
CNP is more general, in that it encapsulates the high-level<br />
statistics of a family of functions. As such it constitutes a<br />
high-level abstraction that can be reused for multiple tasks.<br />
In future work, they are going to explore how far these models can<br />
help in tackling the many key machine learning problems<br />
that seem to hinge on abstraction, such as transfer learning,<br />
meta-learning, and data efficiency.<br />
<br />
== Critiques ==<br />
<br />
This paper introduces a method, for reducing the computational complexity of the more famous Gaussian Processes model, but they have mentioned a complexity of O(n + m) which is almost the same order of RBF kernel GP. With respect to performances in a sequence of tasks, the authors have not made metric comparisons to GP methods to prove the superiority of their approach.<br />
<br />
It appears that the proposed model is effective in making accurate predictions using lower quality inputs. For example, a dataset with fewer data points or an image with fewer pixels. However, it is not clear whether the proposed algorithm can be trained with a smaller amount of input data.<br />
<br />
== Other Sources ==<br />
# Code for this model and a simpler explanation can be found at [https://github.com/deepmind/conditional-neural-process]<br />
# A newer version of the model is described in this paper [https://arxiv.org/pdf/1807.01622.pdf]<br />
# A good blog post on neural processes [https://kasparmartens.rbind.io/post/np/]<br />
<br />
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2013.</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=policy_optimization_with_demonstrations&diff=41655policy optimization with demonstrations2018-11-27T21:40:52Z<p>Aghabuss: </p>
<hr />
<div>= Introduction =<br />
<br />
The reinforcement learning (RL) method has made significant progress in a variety of applications, but the exploration problems regarding how to gain more experience from novel policies to improve long-term performance are still challenges, especially in environments where reward signals are sparse and rare. There are currently two ways to solve such exploration problems in RL: <br />
<br />
1) Guide the agent to explore states that have never been seen. <br />
<br />
2) Guide the agent to imitate a demonstration trajectory sampled from an expert policy to learn. <br />
<br />
When guiding the agent to imitate the expert behavior for learning, there are also two methods: putting the demonstration directly into the replay memory [1] [2] [3] or using the demonstration trajectory to pre-train the policy in a supervised manner [4]. However, neither of these methods takes full advantage of the demonstration data. They instead treat the demonstration data identically to self-generated data, requiring a tremendous number of difficult to collect examples to learn effectively. To address this problem, a novel policy optimization method from demonstration (POfD) is proposed, which takes full advantage of the demonstration and there is no need to ensure that the expert policy is the optimal policy. To summarize, the authors bring forth this idea through the following techniques:<br />
<br />
1) A demonstration guided exploration term measuring the divergence between current and the expert policy is added to the policy optimization objective, increasing the similarity to expert-like exploration.<br />
<br />
2) They say that for better learning from demonstrations and getting an optimization friendly lower bound, the proposed objective could be defined on an occupancy measure as in [14].<br />
<br />
3) Finally, they show that the optimization can move towards optimizing the derived lower bound and the generative adversarial training.<br />
<br />
The authors also evaluate the performance of POfD on Mujoco [5] in sparse-reward environments. The experiments results show that the performance of POfD is greatly improved compared with some strong baselines and even to the policy gradient method in dense-reward environments.<br />
<br />
==Intuition==<br />
The agent should imitate the demonstrated behavior when rewards are sparse and then explore new states on its own after acquiring sufficient skills, which is a dynamic intrinsic reward mechanism that can be reshaped in terms of the native rewards in RL. At present, the state of the art exploration in Reinforcement learning is simply epsilon greedy which just makes random moves for a small percentage of times to explore unexplored moves. This is very naive and is one of the main reasons for the high sample complexity in RL. On the other hand, if there is an expert demonstrator who can guide exploration, the agent can make more guided and accurate exploratory moves.<br />
<br />
=Related Work =<br />
There are some related works in overcoming exploration difficulties by learning from demonstration [6] and imitation learning in RL.<br />
<br />
For learning from demonstration (LfD),<br />
# Most LfD methods adopt value-based RL algorithms, such as DQfD (Deep Q-learning from Demonstrations) [2] which are applied into the discrete action spaces and DDPGfD (Deep Deterministic Policy Gradient from Demonstrations) [3] which extends this idea to the continuous spaces. But both of them under-utilize the demonstration data.<br />
# There are some methods based on policy iteration [7] [8], which shapes the value function by using demonstration data. But they get the bad performance when demonstration data is imperfect.<br />
# A hybrid framework [9] that learns the policy in which the probability of taking demonstrated actions is maximized is proposed, which considers fewer demonstration data.<br />
# A reward reshaping mechanism [10] that encourages taking actions close to the demonstrated ones is proposed. It is similar to the method in this paper, but there exist some differences as it is defined as a potential function based on multi-variate Gaussian to model the distribution of state-actions.<br />
All of the above methods require a lot of perfect demonstrations to get satisfactory performance, which is different from POfD in this paper.<br />
<br />
For imitation learning, <br />
# Inverse Reinforce Learning [11] problems are solved by alternating between fitting the reward function and selecting the policy [12] [13]. But it cannot be extended to big-scale problems.<br />
# Generative Adversarial Imitation Learning (GAIL) [14] uses a discriminator to distinguish whether a state-action pair is from the expert or the learned policy and it can be applied into the high-dimensional continuous control problems.<br />
<br />
Both of the above methods are effective for imitation learning, but cannot leverage the valuable feedback given by the environments and usually suffer from bad performance when the expert data is imperfect. That is different from POfD in this paper.<br />
<br />
There is also another idea in which an agent learns using hybrid imitation learning and reinforcement learning reward[23, 24]. However, unlike this paper, they did not provide some theoretical support for their method and only explained some intuitive explanations.<br />
<br />
=Background=<br />
<br />
==Preliminaries==<br />
Markov Decision Process (MDP) [15] is defined by a tuple <math>⟨\mathcal{S}, \mathcal{A}, \mathcal{P}, r, \gamma⟩ </math>, where <math>\mathcal{S}</math> is the state space, <math>\mathcal{A} </math> is the action space, <math>\mathcal{P}(s'|s,a)</math> is the transition distribution of taking action <math> a </math> at state <math>s </math>, <math> r(s,a) </math>is the reward function, and <math> \gamma </math> is the discount factor between 0 and 1. Policy <math> \pi(a|s) </math> is a mapping from state to action probabilities, the performance of <math> \pi </math> is usually evaluated by its expected discounted reward <math> \eta(\pi) </math>: <br />
\[\eta(\pi)=\mathbb{E}_{\pi}[r(s,a)]=\mathbb{E}_{(s_0,a_0,s_1,...)}[\sum_{t=0}^\infty\gamma^{t}r(s_t,a_t)] \]<br />
The value function is <math> V_{\pi}(s) =\mathbb{E}_{\pi}[r(·,·)|s_0=s] </math>, the action value function is <math> Q_{\pi}(s,a) =\mathbb{E}_{\pi}[r(·,·)|s_0=s,a_0=a] </math>, and the advantage function that reflects the expected additional reward after taking action a at state s is <math> A_{\pi}(s,a)=Q_{\pi}(s,a)-V_{\pi}(s)</math>.<br />
Then the authors define Occupancy measure, which is used to estimate the probability that state <math>s</math> and state action pairs <math>(s,a)</math> when executing a certain policy.<br />
[[File:def1.png|500px|center]]<br />
Then the performance of <math> \pi </math> can be rewritten to: <br />
[[File:equ2.png|500px|center]]<br />
At the same time, the authors propose a lemma: <br />
[[File:lemma1.png|500px|center]]<br />
<br />
==Problem Definition==<br />
Generally, RL tasks and environments do not provide a comprehensive reward and instead rely on sparse feedback indicating whether the goal is reached.<br />
<br />
In this paper, the authors aim to develop a method that can boost exploration by leveraging effectively the demonstrations <math>D^E </math>from the expert policy <math> \pi_E </math> and maximize <math> \eta(\pi) </math> in the sparse-reward environment. The authors define the demonstrations <math>D^E=\{\tau_1,\tau_2,...,\tau_N\} </math>, where the i-th trajectory <math>\tau_i=\{(s_0^i,a_0^i),(s_1^i,a_1^i),...,(s_T^i,a_T^i)\} </math> is generated from the unknown expert policy <math>\pi_E </math>. In addition, there is an assumption on the quality of the expert policy:<br />
[[File:asp1.png|500px|center]]<br />
<br />
<br />
Throughout the paper, they use <math>\pi_E </math> to denote the expert policy that gives the relatively good <math>\eta_\pi </math>, and use <math>\hat{\mathbb{E}}_D </math>to denote empirical expectation estimated from the demonstrated trajectories <math>D^E </math>. We have the following reasonable and necessary assumption on the quality of the expert policy <math>\pi_E </math>.<br />
<br />
<br />
Moreover, it is not necessary to ensure that the expert policy is advantageous over all the policies. This is because that POfD will learn a better policy than expert policy by exploring on its own in later learning stages.<br />
<br />
=Method=<br />
<br />
==Policy Optimization with Demonstration (POfD)==<br />
<br />
[[File:ff1.png|thumb|500px|center |Figure 1: Demonstrations (the blue curve) enables POfD to explore in the high-reward regions (red arrows). On the other hand random explorations (olive green dashed curves) occur in sparse-reward environments.]]<br />
<br />
This method optimizes the policy by forcing the policy to explore in the nearby region of the expert policy that is specified by several demonstrated trajectories <math>D^E </math> (as shown in Fig.1) in order to avoid causing slow convergence or failure when the environment feedback is sparse. In addition, the authors encourage the policy π to explore by "following" the demonstrations <math>D^E </math>. Thus, a new learning objective is given:<br />
\[ \mathcal{L}(\pi_{\theta})=-\eta(\pi_{\theta})+\lambda_{1}D_{JS}(\pi_{\theta},\pi_{E})\]<br />
where <math>D_{JS}(\pi_{\theta},\pi_{E})</math> is Jensen-Shannon divergence between current policy <math>\pi_{\theta}</math> and the expert policy <math>\pi_{E}</math> , <math>\lambda_1</math> is a trading-off parameter, and <math>\theta</math> is policy parameter. According to Lemma 1, the authors use <math>D_{JS}(\rho_{\theta},\rho_{E})</math> to instead of <math>D_{JS}(\pi_{\theta},\pi_{E})</math>, because it is easier to optimize through adversarial training on demonstrations. The learning objective is: <br />
\[ \mathcal{L}(\pi_{\theta})=-\eta(\pi_{\theta})+\lambda_{1}D_{JS}(\rho_{\theta},\rho_{E})\]<br />
<br />
==Benefits of Exploration with Demonstrations==<br />
The authors introduce the benefits of POfD. Firstly, we consider the expression of expected return in policy gradient methods [16].<br />
\[ \eta(\pi)=\eta(\pi_{old})+\mathbb{E}_{\tau\sim\pi}[\sum_{t=0}^\infty\gamma^{t}A_{\pi_{old}}(s,a)]\]<br />
<math>\eta(\pi)</math>is the advantage over the policy <math>\pi_{old}</math> in the previous iteration, so the expression can be rewritten by<br />
\[ \eta(\pi)=\eta(\pi_{old})+\sum_{s}\rho_{\pi}(s)\sum_{a}\pi(a|s)A_{\pi_{old}}(s,a)\]<br />
The local approximation to <math>\eta(\pi)</math> up to first order is usually as the surrogate learning objective to be optimized by policy gradient methods due to the difficulties brought by complex dependency of <math>\rho_{\pi}(s)</math> over <math> \pi </math>:<br />
\[ J_{\pi_{old}}(\pi)=\eta(\pi_{old})+\sum_{s}\rho_{\pi_{old}}(s)\sum_{a}\pi(a|s)A_{\pi_{old}}(s,a)\]<br />
The policy gradient methods improve <math>\eta(\pi)</math> monotonically by optimizing the above <math>J_{\pi_{old}}(\pi)</math> with a sufficiently small update step from <math>\pi_{old}</math> to <math>\pi</math> such that <math>D_{KL}^{max}(\pi, \pi_{old})</math> is bounded [16] [17] [18]. POfD imposes an additional regularization <math>D_{JS}(\pi_{\theta}, \pi_{E})</math> between <math>\pi_\theta</math> and <math>\pi_{E}</math> in order to encourage explorations around regions demonstrated by the expert policy. Theorem 1 shows such benefits,<br />
[[File:them1.png|500px|center]]<br />
<br />
In fact, POfD brings another factor, <math>D_{J S}^{max}(\pi_{i}, \pi_{E})</math>, that would fully use the advantage <math>{\hat \delta}</math>and add improvements with a margin over pure policy gradient methods.<br />
<br />
==Optimization==<br />
<br />
For POfD, the authors choose to optimize the lower bound of the Jensen-Shannon divergence instead of directly optimizing the difficult Jensen-Shannon divergence. This optimization method is compatible with any policy gradient methods. Theorem 2 gives the lower bound of <math>D_{JS}(\rho_{\theta}, \rho_{E})</math>：<br />
[[File:them2.png|450px|center]]<br />
Thus, the occupancy measure matching objective can be written as:<br />
[[File:eqnlm.png|450px|center]]<br />
where <math> D(s,a)=\frac{1}{1+e^{-U(s,a)}}: \mathcal{S}\times \mathcal{A} \rightarrow (0,1)</math> is an arbitrary mapping function followed by a sigmoid activation function used for scaling, and its supremum ranging is like a discriminator for distinguishing whether the state-action pair is a current policy or an expert policy.<br />
To avoid overfitting, the authors add causal entropy <math>−H (\pi_{\theta}) </math> as the regularization term. Thus, the learning objective is: <br />
\[\min_{\theta}\mathcal{L}=-\eta(\pi_{\theta})-\lambda_{2}H(\pi_{\theta})+\lambda_{1} \sup_{{D\in(0,1)}^{S\times A}} \mathbb{E}_{\pi_{\theta}}[\log(D(s,a))]+\mathbb{E}_{\pi_{E}}[\log(1-D(s,a))]\]<br />
At this point, the problem closely resembles the minimax problem related to the Generative Adversarial Networks (GANs) [19]. The difference is that the discriminative model D of GANs is well-trained but the expert policy of POfD is not optimal. Then suppose D is parameterized by w. If it is from an expert policy, <math>D_w</math>is toward 1, otherwise it is toward 0. Thus, the minimax learning objective is:<br />
\[\min_{\theta}\max_{w}\mathcal{L}=-\eta(\pi_{\theta})-\lambda_{2}H (\pi_{\theta})+\lambda_{1}( \mathbb{E}_{\pi_{\theta}}[\log(D_{w}(s,a))]+\mathbb{E}_{\pi_{E}}[\log(1-D_{w}(s,a))])\]<br />
The minimax learning objective can be rewritten by substituting the expression of <math> \eta(\pi) </math>:<br />
\[\min_{\theta}\max_{w}-\mathbb{E}_{\pi_{\theta}}[r'(s,a)]-\lambda_{2}H (\pi_{\theta})+\lambda_{1}\mathbb{E}_{\pi_{E}}[\log(1-D_{w}(s,a))]\]<br />
where <math> r'(s,a)=r(a,b)-\lambda_{1}\log(D_{w}(s,a))</math> is the reshaped reward function.<br />
The above objective can be optimized efficiently by alternately updating policy parameters θ and discriminator parameters w, then the gradient is given by:<br />
\[\mathbb{E}_{\pi}[\nabla_{w}\log(D_{w}(s,a))]+\mathbb{E}_{\pi_{E}}[\nabla_{w}\log(1-D_{w}(s,a))]\]<br />
Then, fixing the discriminator <math>D_w</math>, the reshaped policy gradient is:<br />
\[\nabla_{\theta}\mathbb{E}_{\pi_{\theta}}[r'(s,a)]=\mathbb{E}_{\pi_{\theta}}[\nabla_{\theta}\log\pi_{\theta}(a|s)Q'(s,a)]\]<br />
where <math>Q'(\bar{s},\bar{a})=\mathbb{E}_{\pi_{\theta}}[r'(s,a)|s_0=\bar{s},a_0=\bar{a}]</math>.<br />
<br />
At the end, Algorithm 1 gives the detailed process.<br />
[[File:pofd.png|450px|center]]<br />
<br />
=Discussion on Existing LfD Methods=<br />
<br />
To connect with the proposed POfD method, interpretation of the existing methods DQfD and DDPGfD through occupancy measure matching is provided. Both of the existing methods leverage demonstrations to aid exploration in RL.<br />
<br />
==DQFD==<br />
DQFD [2] puts the demonstrations into a replay memory D and keeps them throughout the Q-learning process. The objective for DQFD is:<br />
\[J_{DQfD}={\hat{\mathbb{E}}}_{D}[(R_t(n)-Q_w(s_t,a_t))^2]+\alpha{\hat{\mathbb{E}}}_{D^E}[(R_t(n)-Q_w(s_t,a_t))^2]\]<br />
The second term can be rewritten as <math> {\hat{\mathbb{E}}}_{D^E}[(R_t(n)-Q_w(s_t,a_t))^2]={\hat{\mathbb{E}}}_{D^E}[(\hat{\rho}_E(s,a)-\rho_{\pi}(s,a))^{2}r^2(s,a)]</math>, which can be regarded as a regularization forcing current policy's occupancy measure to match the expert's empirical occupancy measure, weighted by the potential reward.<br />
<br />
==DDPGfD==<br />
DDPGfD [3] also puts the demonstrations into a replay memory D, but it is based on an actor-critic framework [21]. The objective for DDPGfD is the same as DQFD. Its policy gradient is:<br />
\[\nabla_{\theta}J_{DDPGfD}\approx \mathbb{E}_{s,a}[\nabla_{a}Q_w(s,a)\nabla_{\theta}\pi_{\theta}(s)], a=\pi_{\theta}(s) \]<br />
From this equation, policy is updated relying on learned Q-network <math>Q_w </math>rather than the demonstrations <math>D^{E} </math>. DDPGfD shares the same objective function for <math>Q_w </math> as DQfD, thus they have the same way of leveraging demonstrations, that is the demonstrations in DQfD and DDPGfD induce an occupancy measure matching regularization.<br />
<br />
=Experiments=<br />
<br />
==Goal==<br />
The authors aim at investigating 1) whether POfD can aid exploration by leveraging a few demonstrations, even though the demonstrations are imperfect. 2) whether POfD can succeed and achieve high empirical return, especially in environments where reward signals are sparse and rare. <br />
<br />
==Settings==<br />
The authors conduct the experiments on 8 physical control tasks, ranging from low-dimensional spaces to high-dimensional spaces and naturally sparse environments based on OpenAI Gym [20] and Mujoco (Multi-Joint dynamics with Contact) [5] (Gym is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games like Pong or Pinball. MuJoCo is a physics engine aiming to facilitate research and development in robotics, biomechanics, graphics and animation, and other areas where fast and accurate simulation is needed. In order to get familiar with OpenAI Gym and Mujoco environment, you can watch these videos, respectively: [http://www.mujoco.org/image/home/mujocodemo.mp4 Mujoco], [https://gym.openai.com/v2018-02-21/videos/SpaceInvaders-v0-4184afb3-1223-4ac6-b52b-8e863cbe24a5/original.mp4 OpenAI Gym]). Due to the uniqueness of the environments, the authors introduce 4 ways to sparsify their built-in dense rewards. TYPE1: a reward of +1 is given when the agent reaches the terminal state, and otherwise 0. TYPE2: a reward of +1 is given when the agent survives for a while. TYPE3: a reward of +1 is given for every time the agent moves forward over a specific number of units in Mujoco environments. TYPE4: specially designed for InvertedDoublePendulum, a reward +1 is given when the second pole stays above a specific height of 0.89. The details are shown in Table 1. Moreover, only one single imperfect trajectory is used as the demonstrations in this paper. The authors collect the demonstrations by training an agent insufficiently by running TRPO (Trust Region Policy Optimization) in the corresponding dense environment. <br />
[[File:pofdt1.png|900px|center]]<br />
<br />
==Baselines==<br />
The authors compare POfD against 5 strong baselines:<br />
* training the policy with TRPO [17] in dense environments, which is called expert <br />
* training the policy with TRPO [17] in sparse environments<br />
* applying GAIL [14] to learn the policy from demonstrations<br />
* DQfD [2]<br />
* DDPGfD [3]<br />
<br />
<br />
1. Trust Region Policy Optimization (TRPO) is an iterative procedure for optimizing policies, with guaranteed monotonic improvement. By making several approximations to the theoretically-justified procedure, a practical algorithm such as this can be developed. This algorithm is similar to natural policy gradient methods and is effective for optimizing neural networks.<br />
<br />
2. Generative Adversarial Imitation Learning (GAIL) is a method to directly extract a policy from data as if it were obtained by reinforcement learning and by following inverse reinforcement learning.<br />
<br />
3. Deep Q-learning from Demonstrations (DQfD), is a method that leverages small sets of demonstration data to speed up the learning process from relatively small amounts of demonstration data and is able to automatically assess the necessary ratio of demonstration data while learning thanks to a prioritized replay mechanism.<br />
<br />
4. DDPGfD (Deep Deterministic Policy Gradients From Demonstrations) uses prioritized replay to enable efficient propagation of the reward information, which is essential in problems with sparse rewards.<br />
<br />
==Results==<br />
Firstly, the authors test the performance of POfD in sparse control environments with discrete actions. From Table 1, POfD achieves performance comparable with the policy learned under dense environments. From Figure 2, only POfD successes to explore sufficiently and achieves great performance in both sparse environments. TRPO [17] and DQFD [2] fail to explore and GAIL [14] converges to the imperfect demonstration in MountainCar [22].<br />
<br />
[[File:pofdf2.png|500px|center]]<br />
<br />
Then, the authors test the performance of POfD under spares environments with continuous actions space. From Figure 3, POfD achieves expert-level performance in terms of accumulated rewards and surpasses other strong baselines training the policy with TRPO. By watching the learning process of different methods, we can see that TRPO consistently fails to explore the environments when the feedback is sparse, except for HalfCheetah. This may be because there is no terminal state in HalfCheetah, thus a random agent can perform reasonably well as long as the time horizon is sufficiently long. This is shown in Figure3 where the improvement of TRPO begins to show after 400 iterations. DDPGfD and GAIL have common drawback: during training process, they both converge to the imperfect demonstration data. For HalfCheetah, GAIL fails to converge and DDPGfD converges to an even worse point. This situation is expected because the policy and value networks tend to over-fit when having few data, so the training process of GAIL and DDPGfD is severely biased by the imperfect data. Finally, our proposed method can effectively explore the environment with the help of demonstration-based intrinsic reward reshaping and succeeds consistently across different tasks both in terms of learning stability and convergence speed.<br />
[[File:pofdf3.png|900px|center]]<br />
<br />
The authors also implement a locomotion task <math>Humanoid</math>, which teaches a human-like robot to walk. The state space of dimension is 376, which is very hard to render. As a result, POfD still outperformed all three baselike methods, as they failed to learn policies in such a sparse reward environment.<br />
<br />
The reacher environment is a task that the target is to control a robot arm to touch an object. the location of the object is random for each instantiation. The environment reward is sparse: every time the arm reaches the ball and holds for a while (e.g., 5 time steps), it receives a reward of +1; otherwise, it gets zero reward. The authors select 15 random trajectories as demonstration data, and the performance of POfD is much better than the expert, while all other baseline methods failed.<br />
<br />
=Conclusion=<br />
In this paper, a method, POfD, is proposed that can acquire knowledge from a limited amount of imperfect demonstration data to aid exploration in environments with sparse feedback. It is compatible with any policy gradient method. POfD induces implicit dynamic reward shaping and brings provable benefits for policy improvement. Moreover, the results of the experiments have shown the validity and effectiveness of POfD in encouraging the agent to explore around the nearby region of the expert policy and learn better policies. The key contribution is that POfD helps the agent work with few and imperfect demonstrations in an environment with sparse rewards.<br />
<br />
=Critique=<br />
# A novel demonstration-based policy optimization method is proposed. In the process of policy optimization, POfD reshapes the reward function. This new reward function can guide the agent to imitate the expert behavior when the reward is sparse and explore on its own when the reward value can be obtained, which can take full advantage of the demonstration data and there is no need to ensure that the expert policy is the optimal policy.<br />
# POfD can be combined with any policy gradient methods. Its performance surpasses five strong baselines and can be comparable to the agents trained in the dense-reward environment.<br />
# The paper is structured and the flow of ideas is easy to follow. For related work, the authors clearly explain similarities and differences among these related works.<br />
# This paper's scalability is demonstrated. The experiments environments are ranging from low-dimensional spaces to high-dimensional spaces and from discrete action spaces to continuous actions spaces. For future work, can it be realized in the real world?<br />
# There is a doubt that whether it is a correct method to use the trajectory that was insufficiently learned in a dense-reward environment as the imperfect demonstration.<br />
# In this paper, the performance only is judged by the cumulative reward, can other evaluation terms be considered? For example, the convergence rate.<br />
# The performance of this algorithm hinges on the assumption that expert demonstrations are near optimal in the action space. As seen in figure 3, there appears to be an upper bound to performance near (or just above) the expert accuracy -- this may be an indication of a performance ceiling. In games where near-optimal policies can differ greatly (e.g.; offensive or defensive strategies in chess), the success of the model will depend on the selection of expert demonstrations that are closest to a truly optimal policy (i.e.; just because a policy is the current expert, it does not mean it resembles the true optimal policy).<br />
<br />
=References=<br />
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[11] Ng, A. Y., Russell, S. J., et al. Algorithms for inverse reinforcement learning. In Icml, pp. 663–670, 2000.<br />
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[12] Syed, U. and Schapire, R. E. A game-theoretic approach to apprenticeship learning. In Advances in neural informa- tion processing systems, pp. 1449–1456, 2008.<br />
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[13] Syed, U., Bowling, M., and Schapire, R. E. Apprenticeship learning using linear programming. In Proceedings of the 25th international conference on Machine learning, pp. 1032–1039. ACM, 2008.<br />
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[14] Ho, J. and Ermon, S. Generative adversarial imitation learn- ing. In Advances in Neural Information Processing Sys- tems, pp. 4565–4573, 2016.<br />
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[15] Sutton, R. S. and Barto, A. G. Reinforcement learning: An introduction, volume 1. MIT press Cambridge, 1998.<br />
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[16] Kakade, S. M. A natural policy gradient. In Advances in neural information processing systems, pp. 1531–1538, 2002.<br />
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[17] Schulman, J., Levine, S., Abbeel, P., Jordan, M., and Moritz, P. Trust region policy optimization. In Proceedings of the 32nd International Conference on Machine Learning (ICML-15), pp. 1889–1897, 2015.<br />
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[18] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017.<br />
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[19] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. Generative adversarial nets. In Advances in neural information processing systems, pp. 2672–2680, 2014.<br />
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[20] Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W. Openai gym, 2016.<br />
<br />
[21] Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971, 2015.<br />
<br />
[22] Moore, A. W. Efficient memory-based learning for robot control. 1990.<br />
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[23] Zhu, Y., Wang, Z., Merel, J., Rusu, A., Erez, T., Cabi, S., Tunyasuvunakool, S., Kramar, J., Hadsell, R., de Freitas, N., et al. Reinforcement and imitation learning for diverse visuomotor skills. arXiv preprint arXiv:1802.09564, 2018.<br />
<br />
[24] Li, Y., Song, J., and Ermon, S. Infogail: Interpretable imitation learning from visual demonstrations. In Advances in Neural Information Processing Systems, pp. 3815–3825, 2017.</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Visual_Reinforcement_Learning_with_Imagined_Goals&diff=41654Visual Reinforcement Learning with Imagined Goals2018-11-27T21:37:51Z<p>Aghabuss: </p>
<hr />
<div>Video and details of this work are available [https://sites.google.com/site/visualrlwithimaginedgoals/ here]<br />
<br />
=Introduction and Motivation=<br />
<br />
Humans are able to accomplish many tasks without any explicit or supervised training, simply by exploring their environment. We are able to set our own goals and learn from our experiences, and thus are able to accomplish specific tasks without ever having been trained explicitly for them. It would be ideal if an autonomous agent can also set its own goals and learn from its environment.<br />
<br />
In the paper “Visual Reinforcement Learning with Imagined Goals”, the authors are able to devise such an unsupervised reinforcement learning system. They introduce a system that sets abstract (self-generated) goals and autonomously learns to achieve those goals. They then show that the system can use these autonomously learned skills to perform a variety of user-specified goals, such as pushing objects, grasping objects, and opening doors, without any additional learning. Lastly, they demonstrate that their method is efficient enough to work in the real world on a Sawyer robot. The robot learns to set and achieve goals with only images as the input to the system.<br />
<br />
The algorithm proposed by the authors is summarized below. A Variational Auto Encoder (VAE) on the (left) learns a latent representation of images gathered during training time (center). These latent variables are used to train a policy on imagined goals (center), which can then be used for accomplishing user-specified goals (right).<br />
<br />
[[File: WF_Sec_11Nov25_01.png |center| 800px]]<br />
<br />
=Related Work =<br />
<br />
Many previous works on vision-based deep reinforcement learning for robotics studied a variety of behaviors such as grasping [1], pushing [2], navigation [3], and other manipulation tasks [4]. However, their assumptions on the models limit their suitability for training general-purpose robots. Some previous works such as Levine et al. [11] proposed time-varying models which require episodic setups and thus are hard to generalize to non-episodic and continuous learning scenarios. There are also other works such as Pinto et al. [12] that proposed an approach using goal images, but it requires instrumented training simulations. Lillicrap et al. [13] use fully model-free training (Model-based RL uses experience to construct an internal model of the transitions and immediate outcomes in the environment. Appropriate actions are then chosen by searching or planning in this world model. Model-free RL, on the other hand, uses experience to learn directly one or both of two simpler quantities (state/action values or policies) which can achieve the same optimal behavior but without estimation or use of a world model. Given a policy, a state has a value, defined in terms of the future utility that is expected to accrue starting from that state [https://www.princeton.edu/~yael/Publications/DayanNiv2008.pdf Reinforcement learning: The Good, The Bad and The Ugly].), but does not learn goal-conditioned skills. The authors' experiments indicate that this technique is difficult to extend to goal-conditioned setting<br />
with image inputs. There are currently no examples that use model-free reinforcement learning for learning policies to train on real-world robotic systems without having ground-truth information.<br />
<br />
In this paper, the authors utilize a goal-conditioned value function to tackle more general tasks through goal relabelling, which improves sample efficiency. Goal relabelling is to retroactively relabel samples in the replay buffer with goals sampled from the latent representation. The paper uses sample random goals from learned latent space to use as replay goals for off-policy Q-learning rather than restricting to states seen along the sampled trajectory as was done in the earlier works. Specifically, they use a model-free Q-learning method that operates on raw state observations and actions. This approach allows for a single transition tuple to be converted into potentially infinite valid training examples. <br />
<br />
Unsupervised learning has been used in a number of prior works to acquire better representations of reinforcement learning. In these methods, the learned representation is used as a substitute for the state for the policy. However, these methods require additional information, such as access to the ground truth reward function based on the true state during training time [5], expert trajectories [6], human demonstrations [7], or pre-trained object-detection features [8]. In contrast, the authors learn to generate goals and use the learned representation to get a reward function for those goals without any of these extra sources of supervision.<br />
<br />
=Goal-Conditioned Reinforcement Learning=<br />
<br />
The ultimate goal in reinforcement learning is to learn a policy <math>\pi</math>, that when given a state <math>s_t</math> and goal <math>g</math> (desired state), can dictate the optimal action <math>a_t</math>. The optimal action <math>a_t</math> is defined as an action which maximizes the expected return denoted by <math>R_t</math> and defined as <math>R_t = \mathbb{E}[\sum_{i = t}^T\gamma^{(i-t)}r_i]</math>, where <math>r_i = r(s_i, a_i, s_{i+1})</math> is the reward for performing action <math>a_i</math> when the current state is <math>s_i</math> and the goal state is <math>s_{i+1}</math> and <math>\gamma</math> is a discount factor which determines the relative importance given to rewards at different times. <br />
<br />
In this paper, goals are not explicitly defined during training. If a goal is not explicitly defined, the agent must be able to generate a set of synthetic goals automatically. Suppose we let an autonomous agent explore an environment with a random policy. After executing each action, start and stop state observations are collected and stored. All state observations are images. For training, the agent can randomly select starting states and goals images from the set of state observations.<br />
<br />
Now given a set of all possible states, a goal, and an initial state, a reinforcement learning framework can be used to find the optimal policy such that a chosen value function is maximized. However, to implement such a framework, a reward function needs to be defined. One choice for the reward is the negative distance between the current state and the goal state, so that maximizing the reward corresponds to minimizing the distance to the goal state.<br />
<br />
[[File:human-giving-goal.png|center|thumb|400px|The task: Make the world look like this image. [9]]]<br />
<br />
In reinforcement learning, a goal-conditioned Q-function can be used to find a single policy to maximize rewards and therefore reach goal states. A goal-conditioned Q-function <math>Q(s,a,g)</math> tells us how good an action <math>a</math> is, given the current state <math>s</math> and goal <math>g</math>. For example, a Q-function tells us, “How good is it to move my hand up (action <math>a</math>), if I’m holding a plate (state <math>s</math>) and want to put the plate on the table (goal <math>g</math>)?” Once this Q-function is trained, a goal-conditioned policy can be obtained by performing the following optimization<br />
<br />
<div align="center"><br />
<math>\pi(s,g) = max_a Q(s,a,g)</math><br />
</div><br />
<br />
which effectively says, “choose the best action according to this Q-function.” By using this procedure, one can obtain a policy that maximizes the sum of rewards, i.e. reaches various goals.<br />
<br />
The reason why Q-learning is popular is that it can be trained in an off-policy manner. Therefore, the only things a Q-function needs are samples of state, action, next state, goal, and reward <math>(s,a,s′,g,r)</math>. This data can be collected by any policy and can be reused across multiples tasks. So a preliminary goal-conditioned Q-learning algorithm looks like this:<br />
<br />
[[File:ql.png|center|600px]]<br />
<br />
From the tuple <math>(s,a,s',g,r)</math>, an approximate Q-function paramaterized by <math>w</math> can be trained by minimizing the Bellman error:<br />
<br />
<div align="center"><br />
<math>\mathcal{E}(w) = \frac{1}{2} || Q_w(s,a,g) -(r + \gamma \max_{a'} Q_{\overline{w}}(s',a',g)) ||^2 </math><br />
</div><br />
<br />
where <math>\overline{w}</math> is treated as some constant.<br />
<br />
The main drawback in this training procedure is collecting data. In theory, one could learn to solve various tasks without even interacting with the world if more data are available. Unfortunately, it is difficult to learn an accurate model of the world, so sampling is usually performed to get state-action-next-state data, <math> (s,a,s′)</math> . However, if the reward function <math>r(s,g)</math> can be accessed, one can retroactively relabel goals and recompute rewards. This way, more data can be artificially generated given a single <math>(s,a,s′)</math> tuple. As a result, the training procedure can be modified like so:<br />
<br />
[[File:qlr.png|center|600px]]<br />
<br />
This goal resampling makes it possible to simultaneously learn how to reach multiple goals at once without needing more data from the environment. Thus, this simple modification can result in substantially faster learning. However, the method described above makes two major assumptions: (1) you have access to a reward function and (2) you have access to a goal sampling distribution <math>p(g)</math>. When moving to vision-based tasks where goals are images, both of these assumptions introduce practical concerns, as the task of generating goal images is fairly intensive.<br />
<br />
For one, a fundamental problem with this reward function is that it assumes that the distance between raw images will yield semantically useful information. But images are noisy and a large amount of information in an image may not be related to the object we analyze. Thus, the distance between the two images may not correlate with their semantic distance.<br />
<br />
Second, because the goals are images, a goal image distribution <math>p(g)</math> is needed so that one can sample goal images. Manually designing a distribution over goal images is a non-trivial task and image generation is still an active field of research. It would be ideal if the agent can autonomously imagine its own goals and learn how to reach them.<br />
<br />
Retroactively generating goals is also explored in tabular domains in [15]and in continuous domains in [14] using hindsight experience replay (HER). However, HER is<br />
limited to sampling goals seen along a trajectory, which greatly limits the number and diversity of goals with which one can relabel a given transition.<br />
<br />
=Variational Autoencoder=<br />
An autoencoder is a type of machine learning model that can learn to extract a robust, space-efficient feature vector from an image. This generative model converts high-dimensional observations <math>x</math>, like images, into low-dimensional latent variables <math>z</math>, and vice versa. The model is trained so that the latent variables capture the underlying factors of variation in an image. A current image <math>x</math> and goal image <math>x_g</math> can be converted into latent variables <math>z</math> and <math>z_g</math>, respectively. These latent variables can then be used to represent the state and goal for the reinforcement learning algorithm. Learning Q functions and policies on top of this low-dimensional latent space rather than directly on images result in faster learning.<br />
<br />
[[File:robot-interpreting-scene.png|center|thumb|600px|The agent encodes the current image (<math>x</math>) and goal image (<math>x_g</math>) into a latent space and use distances in that latent space for reward. [9]]]<br />
<br />
Using the latent variable representations for the images and goals also solves the problem of computing rewards. Instead of using pixel-wise error as our reward, the distance in the latent space is used as the reward to train the agent to reach a goal. The paper shows that this corresponds to rewarding reaching states that maximize the probability of the latent goal <math>z_g</math>.<br />
<br />
This generative model is also important because it allows an agent to easily generate goals in the latent space. In particular, the authors design the generative model so that latent variables are sampled from the VAE prior. This sampling mechanism is used for two reasons: First, it provides a mechanism for an agent to set its own goals. The agent simply samples a value for the latent variable from the generative model and tries to reach that latent goal. Second, this resampling mechanism is also used to relabel goals as mentioned above. Since the VAE prior is trained by real images, meaningful latent goals can be sampled from the latent variable prior. This will help the agent set its own goals and practice towards them if no goal is provided at test time.<br />
<br />
[[File:robot-imagining-goals.png|center|thumb|600px|Even without a human providing a goal, our agent can still generate its own goals, both for exploration and for goal relabeling. [9]]]<br />
<br />
The authors summarize the purpose of the latent variable representation of images as follows: (1) captures the underlying factors of a scene, (2) provides meaningful distances to optimize, and (3) provides an efficient goal sampling mechanism which can be used by the agent to generate its own goals. The overall method is called reinforcement learning with imagined goals (RIG) by the authors.<br />
The process involves starts with collecting data through a simple exploration policy. Possible alternative explorations could be employed here including off-the-shelf exploration bonuses or unsupervised reinforcement learning methods. Then, a VAE latent variable model is trained on state observations and fine-tuned during training. The latent variable model is used for multiple purposes: sampling a latent goal <math>z_g</math> from the model and conditioning the policy on this goal. All states and goals are embedded using the model’s encoder and then used to train the goal-conditioned value function. The authors then resample goals from the prior and compute rewards in the latent space.<br />
<br />
=Algorithm=<br />
[[File:algorithm1.png|center|thumb|600px|]]<br />
<br />
Algorithm 1 is called reinforcement learning with imagined goals (RIG). The data is first collected via a simple exploration policy. The proposed model allows for alternate exploration policies to be used which include off-the-shelf exploration bonuses or unsupervised reinforcement learning methods. Then, the authors train a VAE latent variable model on state observations and finetune it over the course of training. VAE latent space modeling is used to allow the conditioning of policy on the goal which is sampled from the latent model. The VAE model is also used to encode all the goals and the states. When the goal-conditioned value function is trained, the authors resample prior goals and compute rewards in the latent space using the equation <math display="inline"> r(s, g) = - || z - z_g ||_A \propto \sqrt{log(e_{\Phi}(z_g | s))} </math>.<br />
<br />
This equation is derived from the equation below. This is based on the choice to use the negative Mahalanobis distance in the latent space for the reward:<br />
<br />
<math display="inline"> r(s, g) = - || e(s) - e(g) ||_A = - || z - z_g ||_A </math><br />
<br />
=Experiments=<br />
<br />
The authors evaluated their method against some prior algorithms and ablated versions of their approach on a suite of simulated and real-world tasks: Visual Reacher, Visual Pusher, and Visual Multi-Object Pusher. They compared their model with the following prior works: L&R, DSAE, HER, and Oracle. It is concluded that their approach substantially outperforms the previous methods and is close to the state-based "oracle" method in terms of efficiency and performance.<br />
<br />
The figure below shows the performance of different algorithms on this task. This involved a simulated environment with a Sawyer arm. The authors' algorithm was given only visual input, and the available controls were end-effector velocity. The plots show the distance to the goal state as a function of simulation steps. The Oracle, as a baseline, was given true object location information, as opposed to visual pixel information.<br />
<br />
[[File:WF_Sec_11Nov_25_02.png|1000px]]<br />
<br />
<br />
They then investigated the effectiveness of distances in the VAE latent space for the Visual Pusher task. They observed that latent distance significantly outperforms the log probability and pixel mean-squared error. The resampling strategies are also varied while fixing other components of the algorithm to study the effect of relabeling strategy. In this experiment, the RIG, which is an equal mixture of the VAE and Future sampling strategies, performs best. Subsequently, learning with variable numbers of objects was studied by evaluating on a task where the environment, based on the Visual Multi-Object Pusher, randomly contains zero, one, or two objects during testing. The results show that their model can tackle this task successfully.<br />
<br />
Finally, the authors tested the RIG in a real-world robot for its ability to reach user-specified positions and push objects to desired locations, as indicated by a goal image. The robot is trained with access only to 84x84 RGB images and without access to joint angles or object positions. The robot first learns by settings its own goals in the latent space and autonomously practices reaching different positions without human involvement. After a reasonable amount of time of training, the robot is given a goal image. Because the robot has practiced reaching so many goals, it is able to reach this goal without additional training:<br />
<br />
[[File:reaching.JPG|center|thumb|600px|(Left) The robot setup is pictured. (Right) Test rollouts of the learned policy.]]<br />
<br />
The method for reaching only needs 10,000 samples and an hour of real-world interactions.<br />
<br />
They also used RIG to train a policy to push objects to target locations:<br />
<br />
[[File:pushing.JPG|center|thumb|600px|The robot pushing setup is<br />
pictured, with frames from test rollouts of the learned policy.]]<br />
<br />
The pushing task is more complicated and the method requires about 25,000 samples. Since the authors do not have the true position during training, so they used test episode returns as the VAE latent distance reward. As learning proceeds, RIG makes steady progress at optimizing the latent distance.<br />
<br />
=Conclusion & Future Work=<br />
<br />
In this paper, a new RL algorithm is proposed to efficiently solve goal-conditioned, vision-based tasks without any ground truth state information or reward functions. The author suggests that one could instead use other representations, such as language and demonstrations, to specify goals. Also, while the paper provides a mechanism to sample goals for autonomous exploration, one can combine the proposed method with existing work by choosing these goals in a more principled way, i.e. a procedure that is not only goal-oriented, but also information seeking or uncertainty aware, to perform even better exploration. Furthermore, combining the idea of this paper with methods from multitask learning and meta-learning is a promising path to create general-purpose agents that can continuously and efficiently acquire skill. Lastly, there are a variety of robot tasks whose state representation would be difficult to capture with sensors, such as manipulating deformable objects or handling scenes with variable number of objects. It is interesting to see whether the RIG can be scaled up to solve these tasks. A new paper [10] was published last week that built on the framework of goal conditioned Reinforcement Learning to extract state representations based on the actions required to reach them, which is abbreviated ARC for actionable representation for control.<br />
<br />
=Critique=<br />
1. This paper is novel because it uses visual data and trains in an unsupervised fashion. The algorithm has no access to a ground truth state or to a pre-defined reward function. It can perform well in a real-world environment with no explicit programming.<br />
<br />
2. From the videos, one major concern is that the output of robotic arm's position is not stable during training and test time. It is likely that the encoder reduces the image features too much so that the images in the latent space are too blurry to be used goal images. It would be better if this can be investigated in the future. It would be better, if a method is investigated with multiple data sources, and the agent is trained to choose the source which has more complete information. <br />
<br />
3. The algorithm seems to perform better when there is only one object in the images. For example, in Visual Multi-Object Pusher experiment, the relative positions of two pucks do not correspond well with the relative positions of two pucks in goal images. The same situation is also observed in Variable-object experiment. We may guess that the more information contained in an image, the less likely the robot will perform well. This limits the applicability of the current algorithm to solving real-world problems.<br />
<br />
4. The instability mentioned in #2 is even more apparent in the multi-object scenario and appears to result from the model attempting to optimize on the position of both objects at the same time. Reducing the problem to a sequence of single-object targets may reduce the amount of time the robots spend moving between the multiple objects in the scene (which it currently does quite frequently).<br />
<br />
=References=<br />
1. Lerrel Pinto, Marcin Andrychowicz, Peter Welinder, Wojciech Zaremba, and Pieter Abbeel. Asymmetric<br />
Actor Critic for Image-Based Robot Learning. arXiv preprint arXiv:1710.06542, 2017.<br />
<br />
2. Pulkit Agrawal, Ashvin Nair, Pieter Abbeel, Jitendra Malik, and Sergey Levine. Learning to Poke by<br />
Poking: Experiential Learning of Intuitive Physics. In Advances in Neural Information Processing Systems<br />
(NIPS), 2016.<br />
<br />
3. Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Yide Shentu, Evan<br />
Shelhamer, Jitendra Malik, Alexei A Efros, and Trevor Darrell. Zero-Shot Visual Imitation. In International<br />
Conference on Learning Representations (ICLR), 2018.<br />
<br />
4. Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David<br />
Silver, and Daan Wierstra. Continuous control with deep reinforcement learning. In International<br />
Conference on Learning Representations (ICLR), 2016.<br />
<br />
5. Irina Higgins, Arka Pal, Andrei A Rusu, Loic Matthey, Christopher P Burgess, Alexander Pritzel, Matthew<br />
Botvinick, Charles Blundell, and Alexander Lerchner. Darla: Improving zero-shot transfer in reinforcement<br />
learning. International Conference on Machine Learning (ICML), 2017.<br />
<br />
6. Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, and Chelsea Finn. Universal Planning<br />
Networks. In International Conference on Machine Learning (ICML), 2018.<br />
<br />
7. Pierre Sermanet, Corey Lynch, Yevgen Chebotar, Jasmine Hsu, Eric Jang, Stefan Schaal, and Sergey<br />
Levine. Time-contrastive networks: Self-supervised learning from video. arXiv preprint arXiv:1704.06888,<br />
2017.<br />
<br />
8. Alex Lee, Sergey Levine, and Pieter Abbeel. Learning Visual Servoing with Deep Features and Fitted<br />
Q-Iteration. In International Conference on Learning Representations (ICLR), 2017.<br />
<br />
9. Online source: https://bair.berkeley.edu/blog/2018/09/06/rig/<br />
<br />
10. https://arxiv.org/pdf/1811.07819.pdf<br />
<br />
11. Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel. End-to-End Training of Deep Visuomotor Policies. Journal of Machine Learning Research (JMLR), 17(1):1334–1373, 2016. ISSN 15337928.<br />
<br />
12. Lerrel Pinto, Marcin Andrychowicz, Peter Welinder, Wojciech Zaremba, and Pieter Abbeel. Asymmetric Actor Critic for Image-Based Robot Learning. arXiv preprint arXiv:1710.06542, 2017.<br />
<br />
13. Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. Continuous control with deep reinforcement learning. In International Conference on Learning Representations (ICLR), 2016.<br />
<br />
14. Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob Mcgrew, Josh Tobin, Pieter Abbeel, and Wojciech Zaremba. Hindsight Experience Replay. In<br />
Advances in Neural Information Processing Systems (NIPS) 2017.<br />
<br />
15. L P Kaelbling. Learning to achieve goals. In IJCAI-93. Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, volume vol.2, pages 1094 – 8, 1993.</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Unsupervised_Neural_Machine_Translation&diff=41653Unsupervised Neural Machine Translation2018-11-27T21:32:43Z<p>Aghabuss: </p>
<hr />
<div>This paper was published in ICLR 2018, authored by Mikel Artetxe, Gorka Labaka, Eneko Agirre, and Kyunghyun Cho. Open source implementation of this paper is available [https://github.com/artetxem/undreamt here]<br />
<br />
= Introduction =<br />
The paper presents an unsupervised Neural Machine Translation (NMT) method that uses monolingual corpora (single language texts) only. This contrasts with the usual supervised NMT approach which relies on parallel corpora (aligned text) from the source and target languages being available for training. This problem is important because parallel pairing for a majority of languages, e.g. for German-Russian, do not exist. Often, languages can also suffer from having poor resources for translation (e.g. Basque), which could lead to the problem of the dataset being too small (Koehn & Knowles, 2017).<br />
<br />
Other authors have recently tried to address this problem using semi-supervised approaches (small set of parallel corpora). Their approaches have included pivoting or triangulation techniques [Chen et al., 2017], and semi supervised approaches [He, 2016]. However, these methods still require a strong cross-lingual signal. The proposed method eliminates the need for cross-lingual information all together and relies solely on monolingual data. The proposed method builds upon the work done recently on unsupervised cross-lingual embeddings by Artetxe et al., 2017 and Zhang et al., 2017.<br />
<br />
The general approach of the methodology is to:<br />
<br />
# Use monolingual corpora in the source and target languages to learn single language word embeddings for both languages separately.<br />
# Align the 2 sets of word embeddings into a single cross lingual (language independent) embedding.<br />
Then iteratively perform:<br />
# Train an encoder-decoder model to reconstruct noisy versions of sentences in both source and target languages separately. The model uses a single encoder and different decoders for each language. The encoder uses cross lingual word embedding.<br />
# Tune the decoder in each language by back-translating between the source and target language.<br />
<br />
= Background =<br />
<br />
===Word Embedding Alignment===<br />
<br />
The paper uses word2vec [Mikolov, 2013] to convert each monolingual corpora to vector embeddings. They improve the continuous Skip-gram model for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. These embeddings have been shown to contain the contextual and syntactic features independent of language, and so, in theory, there could exist a linear map that maps the embeddings from language L1 to language L2. <br />
<br />
Figure 1 shows an example of aligning the word embeddings in English and French.<br />
<br />
[[File:Figure1_lwali.png|frame|400px|center|Figure 1: the word embeddings in English and French (a & b), and (c) shows the aligned word embeddings after some linear transformation.[Gouws,2016]]]<br />
<br />
Most cross-lingual word embedding methods use bilingual signals in the form of parallel corpora. Usually, the embedding mapping methods train the embeddings in different languages using monolingual corpora, then use a linear transformation to map them into a shared space based on a bilingual dictionary.<br />
<br />
The paper uses the methodology proposed by [Artetxe, 2017] to do cross-lingual embedding aligning in an unsupervised manner and without parallel data. Without going into the details, the general approach of this paper is starting from a seed dictionary of numeral pairings (e.g. 1-1, 2-2, etc.), to iteratively learn the mapping between 2 language embeddings, while concurrently improving the dictionary with the learned mapping at each iteration. This is in contrast to earlier work which used dictionaries of a few thousand words.<br />
<br />
===Other related work and inspirations===<br />
====Statistical Decipherment for Machine Translation====<br />
There has been significant work in statistical deciphering techniques (decipherment is the discovery of the meaning of texts written in ancient or obscure languages or scripts) to develop a machine translation model from monolingual data (Ravi & Knight, 2011; Dou & Knight, 2012). These techniques treat the source language as ciphertext (encrypted or encoded information because it contains a form of the original plaintext that is unreadable by a human or computer without the proper cipher for decoding) and model the generation process of the ciphertext as a two-stage process, which includes the generation of the original English sequence and the probabilistic replacement of the words in it. This approach takes advantage of the incorporation of syntactic knowledge of the languages. The use of word embeddings has also shown improvements in statistical decipherment.<br />
<br />
====Low-Resource Neural Machine Translation====<br />
There are also proposals that use techniques other than direct parallel corpora to do NMT. Some use a third intermediate language that is well connected to the source and target languages independently. For example, if we want to translate German into Russian, we can use English as an intermediate language (German-English and then English-Russian) since there are plenty of resources to connect English and other languages. Johnson et al. (2017) show that a multilingual extension of a standard NMT architecture performs reasonably well for language pairs when no parallel data for the source and target data was used during training. Firat et al. (2016) and Chen et al. (2017) showed that the use of advanced models like teacher-student framework can be used to improve over the baseline of translating using a third intermediate language.<br />
<br />
Other works use monolingual data in combination with scarce parallel corpora. A simple but effective technique is back-translation [Sennrich et al, 2016]. First, a synthetic parallel corpus in the target language is created. Translated sentence and back-translated to the source language and compared with the original sentence.<br />
<br />
The most important contribution to the problem of training an NMT model with monolingual data was from [He, 2016], which trains two agents to translate in opposite directions (e.g. French → English and English → French) and teach each other through reinforcement learning. However, this approach still required a large parallel corpus for a warm start (about 1.2 million sentences), while this paper does not use parallel data.<br />
<br />
= Related Works =<br />
<br />
=== 2.1 UNSUPERVISED CROSS-LINGUAL EMBEDDINGS ===<br />
<br />
A majority of methods for learning cross-lingual word embeddings depend on some bilingual signal at the document level. Embedding mapping methods independently train the embeddings in different languages using monolingual corpora and subsequently learn a linear transformation that maps them to a shared space based on a bilingual dictionary. While the dictionary used in these earlier work typically contains a few thousands entries, Artetxe et al. (2017) propose a simple self-learning extension that gives comparable results with an automatically generated list of numerals, which is used as a shortcut for practical unsupervised learning.<br />
<br />
=== 2.2 STATISTICAL DECIPHERMENT FOR MACHINE TRANSLATION ===<br />
<br />
A considerable body of work in statistical decipherment techniques treat the source language as ciphertext and model the process by which this ciphertext is generated as a two-stage process involving the generation of the original English sequence and the probabilistic replacement of the words in it. The English generative process is modeled using a standard n-gram language model, and the channel model parameters are estimated using either expectation maximization or Bayesian inference. This approach was shown to benefit from the incorporation of syntactic knowledge of the languages involved (Dou & Knight, 2013; Dou et al., 2015). More in line with our proposal, the use of word embeddings has also been shown to bring significant improvements in statistical decipherment for machine translation (Dou et al., 2015).<br />
<br />
=== 2.3 LOW-RESOURCE NEURAL MACHINE TRANSLATION ===<br />
<br />
A simple yet effective approach is to create a synthetic parallel corpus by back-translating a monolingual corpus in the target language (Sennrich et al., 2016a). At the same time, Currey et al. (2017) showed that training an NMT system to directly copy target language text is also helpful and complementary with back-translation. Finally, Ramachandran et al. (2017) pre-train the encoder and the decoder in language modeling. Another method trains two agents to translate in opposite directions (e.g. French → English and English → French), and make them teach each other through a reinforcement learning process. This approach still requires a parallel corpus of a considerable size for a good start.<br />
<br />
= Methodology =<br />
<br />
The corpora data is first preprocessed in a standard way to tokenize and case the words. The authors also experimented with an alternate way of tokenizing words by using Byte-Pair Encoding (BPE) [Sennrich, 2016] (Byte pair encoding or digram coding is a simple form of data compression in which the most common pair of consecutive bytes of data is replaced with a byte that does not occur within that data). BPE has been shown to improve embeddings of rare-words. The vocabulary was limited to the most frequent 50,000 tokens (BPE tokens or words).<br />
<br />
The tokens were then converted to word embeddings using word2vec with 300 dimensions and then aligned between languages using the method proposed by [Artetxe, 2017]. The alignment method proposed by [Artetxe, 2017] is also used as a baseline to evaluate this model as discussed later in Results.<br />
<br />
The translation model uses a standard encoder-decoder model with attention. The encoder is a 2-layer bidirectional RNN, and the decoder is a 2 layer RNN. All RNNs use GRU cells with 600 hidden units. The encoder is shared by the source and target language, while the decoder is different for each language.<br />
<br />
Although the architecture uses standard models, the proposed system differs from the standard NMT through 3 aspects:<br />
<br />
#Dual structure: NMT usually are built for one direction translations English<math>\rightarrow</math>French or French<math>\rightarrow</math>English, whereas the proposed model trains both directions at the same time translating English<math>\leftrightarrow</math>French.<br />
#Shared encoder: one encoder is shared for both source and target languages in order to produce a representation in the latent space independent of language, and each decoder learns to transform the representation back to its corresponding language. <br />
#Fixed embeddings in the encoder: Most NMT systems initialize the embeddings and update them during training, whereas the proposed system trains the embeddings in the beginning and keeps these fixed throughout training, so the encoder receives language-independent representations of the words. This approach ensures that the encoder only learns how to compose the language independent representations to build representations of the larger phrases. This requires existing unsupervised methods to create embeddings using monolingual corpora as discussed in the background. In the proposed method, even though the embeddings used are cross-lingual, the vocabulary used for each language is different. This way if the same word occurs in two different languages and has a different meaning in the respective languages then each word would get a different vector in the respective languages despite being in the same vector space. <br />
<br />
[[File:Figure2_lwali.png|600px|center]]<br />
<br />
The translation model iteratively improves the encoder and decoder by performing 2 tasks: Denoising, and Back-translation.<br />
<br />
'''Note on the need for alignment:''' To train the decoders (in an admittedly “supervised” manner) we make the assumption that they decode from the same latent space. Thus, given a sentence in either language, it needs to represent it in the same latent space to allow training. However, during the back-translation training, the shared encoder stays fixed. This implies that the encoder needs to be set beforehand. For this reason, the process of embedding and alignment is needed. <br />
<br />
===Denoising===<br />
Random noise is added to the input sentences in order to allow the model to learn some structure of languages. Without noise, the model would simply learn to copy the input word by word. Noise also allows the shared encoder to compose the embeddings of both languages in a language-independent fashion, and then be decoded by the language dependent decoder.<br />
<br />
Denoising works by reconstructing a noisy version of a sentence back into the original sentence in the same language. In mathematical form, if <math>x</math> is a sentence in language L1:<br />
<br />
# Construct <math>C(x)</math>, noisy version of <math>x</math>. In the proposed model, <math>C(x)</math> is constructed by randomly swapping contiguous words. If the length of the input sequence <math>x</math> is <math>N</math>, then a total of <math>\frac{N}{2}</math> such swaps are made.<br />
# Input <math>C(x)</math> into the current iteration of the shared encoder and use decoder for L1 to get reconstructed <math>\hat{x}</math>.<br />
<br />
The training objective is to minimize the cross entropy loss between <math>{x}</math> and <math>\hat{x}</math>.<br />
<br />
In other words, the whole system is optimized to take an input sentence in a given language, encode it using the shared encoder, and reconstruct the original sentence using the decoder of that language.<br />
<br />
The proposed noise function is to perform <math>N/2</math> random swaps of words that are contiguous, where <math>N</math> is the number of words in the sentence. This noise model also helps reduce the reliance of the model on the order of words in a sentence which may be different in the source and target languages. The system will also need to correctly learn the internal structure of a language to decode the sentence into the correct order.<br />
<br />
===Back-Translation===<br />
<br />
With only denoising, the system doesn't have a goal to improve the actual translation. Back-translation works by using the decoder of the target language to create a translation, then encoding this translation and decoding again using the source decoder to reconstruct the original sentence. In mathematical form, if <math>C(x)</math> is a noisy version of sentence <math>x</math> in language L1:<br />
<br />
# Input <math>C(x)</math> into the current iteration of shared encoder and the decoder in L2 to construct translation <math>y</math> in L2,<br />
# Construct <math>C(y)</math>, noisy version of translation <math>y</math>,<br />
# Input <math>C(y)</math> into the current iteration of shared encoder and the decoder in L1 to reconstruct <math>\hat{x}</math> in L1.<br />
<br />
The training objective is to minimize the cross entropy loss between <math>{x}</math> and <math>\hat{x}</math>.<br />
<br />
This approach alleviates issues that would have resulted from the training procedure only dealing with a single language at a time. The corpus of a language is converted to a synthetic translation, and trained to predict the original sentence from this translation. <br />
<br />
Contrary to standard back-translation that uses an independent model to back-translate the entire corpus at once, the system uses mini-batches and the dual architecture to generate pseudo-translations and then train the model with the translation, improving the model iteratively as the training progresses.<br />
<br />
===Training===<br />
<br />
Training is done by alternating these 2 objectives from mini-batch to mini-batch. Each iteration would perform one mini-batch of denoising for L1, another one for L2, one mini-batch of back-translation from L1 to L2, and another one from L2 to L1. The procedure is repeated until convergence. <br />
During decoding, greedy decoding was used at training time for back-translation, but actual inference at test time was done using beam-search with a beam size of 12.<br />
<br />
Optimizer choice and other hyperparameters can be found in the paper.<br />
<br />
=Experiments and Results=<br />
<br />
The model was evaluated using the Bilingual Evaluation Understudy (BLEU) Score, which is typically used to evaluate the quality of the translation, using a reference (ground-truth) translation.<br />
<br />
The paper trained translation model under 3 different settings to compare the performance (Table 1). All training and testing data used was from a standard NMT dataset, WMT'14.<br />
<br />
[[File:Table1_lwali.png|600px|center]]<br />
<br />
The results exhibit that for the proposed system to work properly, back-translation is necessary. The denoising technique alone is below the baseline while big improvements appear when introducing back-translation.<br />
<br />
===Unsupervised===<br />
<br />
The model only has access to monolingual corpora, using the News Crawl corpus with articles from 2007 to 2013. The baseline for unsupervised is the method proposed by [Artetxe, 2017], which was the unsupervised word vector alignment method discussed in the Background section.<br />
<br />
The paper adds each component piece-wise when doing an evaluation to test the impact each piece has on the final score. As shown in Table 1, Unsupervised results compared to the baseline of word-by-word results are strong, with improvement between 40% to 140%. Results also show that back-translation is essential. Denoising doesn't show a big improvement however it is required for back-translation, because otherwise, back-translation would translate nonsensical sentences. The addition of back-translation, however, does show large improvement on all tested cases.<br />
<br />
For the BPE experiment, results show it helps in some language pairs but detract in some other language pairs. This is because while BPE helped to translate some rare words, it increased the error rates in other words. It also did not perform well when translating named entities which occur infrequently.<br />
<br />
===Semi-supervised===<br />
<br />
Since there is often some small parallel data but not enough to train a Neural Machine Translation system, the authors test a semi-supervised setting with the same monolingual data from the unsupervised settings together with either 10,000 or 100,000 random sentence pairs from the News Commentary parallel corpus. The supervision is included to improve the model during the back-translation stage to directly predict sentences that are in the parallel corpus.<br />
<br />
Table 1 shows that the model can greatly benefit from the addition of a small parallel corpus to the monolingual corpora. It is surprising that semi-supervised in row 6 outperforms supervised in row 7, one possible explanation is that both the semi-supervised training set and the test set belong to the news domain, whereas the supervised training set is all domains of corpora.<br />
<br />
===Supervised===<br />
<br />
This setting provides an upper bound to the unsupervised proposed system. The data used was the combination of all parallel corpora provided at WMT 2014, which includes Europarl, Common Crawl and News Commentary for both language pairs plus the UN and the Gigaword corpus for French- English. Moreover, the authors use the same subsets of News Commentary alone to run the separate experiments in order to compare with the semi-supervised scenario.<br />
<br />
The Comparable NMT was trained using the same proposed model except it does not use monolingual corpora, and consequently, it was trained without denoising and back-translation. The proposed model under a supervised setting does much worse than the state of the NMT in row 10, which suggests that adding the additional constraints to enable unsupervised learning also limits the potential performance. To improve these results, the authors also suggest using larger models, longer training times, and incorporating several well-known NMT techniques.<br />
<br />
===Qualitative Analysis===<br />
<br />
[[File:Table2_lwali.png|600px|center]]<br />
<br />
Table 2 shows 4 examples of French to English translations, which shows that the high-quality translations are produced by the proposed system, and this system adequately models non-trivial translation relations. Example 1 and 2 show that the model is able to not only go beyond a literal word-by-word substitution but also model structural differences in the languages (ex.e, it correctly translates "l’aeroport international de Los Angeles" as "Los Angeles International Airport", and it is capable of producing high-quality translations of long and more complex sentences. However, in Example 3 and 4, the system failed to translate the months and numbers correctly and having difficulty with comprehending odd sentence structures, which means that the proposed system has limitations. Especially, the authors point that the proposed model has difficulties to preserve some concrete details from source sentences. Results also show, the proposed model's translation quality often lags behind that of a standard supervised NMT system and also there are also some cases where there are both fluency and adequacy problems that severely hinders understanding the original message from the proposed translation, suggesting that there is still room for improvement and possible future work.<br />
<br />
=Conclusions and Future Work=<br />
<br />
The paper presented an unsupervised model to perform translations with monolingual corpora by using an attention-based encoder-decoder system and training using denoise and back-translation.<br />
<br />
Although experimental results show that the proposed model is effective as an unsupervised approach, there is significant room for improvement when using the model in a supervised way, suggesting the model is limited by the architectural modifications. Some ideas for future improvement include:<br />
*Instead of using fixed cross-lingual word embeddings at the beginning which forces the encoder to learn a common representation for both languages, progressively update the weight of the embeddings as training progresses.<br />
*Decouple the shared encoder into 2 independent encoders at some point during training<br />
*Progressively reduce the noise level<br />
*Incorporate character level information into the model, which might help address some of the adequacy issues observed in our manual analysis<br />
*Use other noise/denoising techniques, and analyze their effect in relation to the typological divergences of different language pairs.<br />
<br />
= Critique =<br />
<br />
While the idea is interesting and the results are impressive for an unsupervised approach, much of the model had actually already been proposed by other papers that are referenced. The paper doesn't add a lot of new ideas but only builds on existing techniques and combines them in a different way to achieve good experimental results. The paper is not a significant algorithmic contribution. <br />
<br />
As pointed out, in order to critically analyze the effect of the algorithm, we need to formulate the algorithm in terms of mathematics.<br />
<br />
The results showed that the proposed system performed far worse than the state of the art when used in a supervised setting, which is concerning and shows that the techniques used creates a limitation and a ceiling for performance.<br />
<br />
Additionally, there was no rigorous hyperparameter exploration/optimization for the model. As a result, it is difficult to conclude whether the performance limit observed in the constrained supervised model is the absolute limit, or whether this could be overcome in both supervised/unsupervised models with the right constraints to achieve more competitive results. <br />
<br />
The best results shown are between two very closely related languages(English and French), and does much worse for English - German, even though English and German are also closely related (but less so than English and French) which suggests that the model may not be successful at translating between distant language pairs. More testing would be interesting to see.<br />
<br />
The results comparison could have shown how the semi-supervised version of the model scores compared to other semi-supervised approaches as touched on in the other works section.<br />
<br />
Their qualitative analysis just checks whether their proposed unsupervised NMT generates a sensible translation. It is limited and it needs further detailed analysis regarding the characteristics and properties of translation which is generated by unsupervised NMT.<br />
<br />
* (As pointed out by an anonymous reviewer [https://openreview.net/forum?id=Sy2ogebAW])Future work is vague: “we would like to detect and mitigate the specific causes…” “We also think that a better handling of rare words…” That’s great, but how will you do these things? Do you have specific reasons to think this, or ideas on how to approach them? Otherwise, this is just hand-waving.<br />
<br />
= References =<br />
#'''[Mikolov, 2013]''' Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. "Distributed representations of words and phrases and their compositionality."<br />
#'''[Artetxe, 2017]''' Mikel Artetxe, Gorka Labaka, Eneko Agirre, "Learning bilingual word embeddings with (almost) no bilingual data".<br />
#'''[Gouws,2016]''' Stephan Gouws, Yoshua Bengio, Greg Corrado, "BilBOWA: Fast Bilingual Distributed Representations without Word Alignments."<br />
#'''[He, 2016]''' Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tieyan Liu, and Wei-Ying Ma. "Dual learning for machine translation."<br />
#'''[Sennrich,2016]''' Rico Sennrich and Barry Haddow and Alexandra Birch, "Neural Machine Translation of Rare Words with Subword Units."<br />
#'''[Ravi & Knight, 2011]''' Sujith Ravi and Kevin Knight, "Deciphering foreign language."<br />
#'''[Dou & Knight, 2012]''' Qing Dou and Kevin Knight, "Large scale decipherment for out-of-domain machine translation."<br />
#'''[Johnson et al. 2017]''' Melvin Johnson,et al, "Google’s multilingual neural machine translation system: Enabling zero-shot translation."<br />
#'''[Zhang et al. 2017]''' Meng Zhang, Yang Liu, Huanbo Luan, and Maosong Sun. "Adversarial training for unsupervised bilingual lexicon induction"<br />
#'''[ Koehn & Knowles, 2017]''' Philipp Koehn and Rebecca Knowles. Six challenges for neural machine translation.<br />
#'''[Chen et al., 2017]''' Yun Chen, Yang Liu, Yong Cheng, and Victor O.K. Li. A teacher-student framework for zero-resource neural machine translation.</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=DCN_plus:_Mixed_Objective_And_Deep_Residual_Coattention_for_Question_Answering&diff=36945DCN plus: Mixed Objective And Deep Residual Coattention for Question Answering2018-10-20T19:55:59Z<p>Aghabuss: </p>
<hr />
<div>== Introduction ==<br />
Question Answering(QA) is one of the challenging computer science tasks that need an understanding of the natural language and the ability to reason efficiently. To accurately answer the question, the model must first have a detailed understanding of the context the question is being asked from. Because the questions are usually very detailed, having a shallow knowledge from the context would lead to poor and unacceptable performance. Moreover, The model should gather all the information provided in the question and match them with its knowledge from the context. Generating the answer is another interesting task. Based on the dataset the model is meant for, the output of the model might be in a completely different form.<br />
In the past years, QA datasets have improved significantly. Previous datasets were really simple and they usually did not simulate a real-world question-answer pair. For example, Children's book test was one of the popular datasets that have been used for QA for a long time. But the real task for this dataset was to just fill empty spaces in given sentences with the appropriate words. During the past years, the importance of the QA tasks and their practical uses encouraged many to gather and crowdsource useful and more realistic datasets. The Stanford Question Answering Dataset(SQuAD), Microsoft MAchine Reading COmprehension Dataset(MS MARCO), and Visual Question Answering Dataset(VQA) are only a few examples of the currently advanced datasets.<br />
As a result of these advancements, many researchers are focusing to improve the performance of the question answering models on these datasets. Deep neural networks were able to outperform the human accuracy on a few of these datasets, but in many cases, there is still a gap between the state-of-the-art and human performance. Previously, Dynamic Coattention Networks(DCN) proved to be efficient on the SQuAD, achieving state-of-the-art performance at the time. In this work, a further modification to DCN has been done which improves the accuracy of the model by proposing a mixed objective that combines cross entropy loss with self-critical policy learning.<br />
<br />
==Overview of previous work==<br />
Most of the current QA models are made from different modules and usually stacked on top of each other. Improving one of the modules would lead to an overall performance of the model. Thus, to evaluate the efficiency of an improvement, researchers usually take a previously submitted model and replace their own improved module with the current one in the model. This is mostly because QA is an interesting discipline and has practical uses.<br />
<br />
#Embedding layer: This layer maps each word (or images in the case of visual QA) to a vector space. There are many options to choose for the embedding layer. While pre-trained GloVes or Word2Vecs showed promising results on many tasks, most models use a combination of GloVe and character level embeddings. The character level embeddings are especially useful when dealing with out-of-vocab words. In the case of dealing with images, the embeddings are usually generated using pre-trained ResNets. Using different embedding layers for images has shown to change the overall performance of the model drastically.<br />
#Contextual_layer: The purpose of this layer is to add more features to each word embedding based on the surrounding words and the context. This layer is not presented in many models including the DCN.<br />
#Attention layer: There has been a lot of investigation on the attention mechanisms in recent years. These works, mostly inspired by Bahdanau et al. (2014), try to either modify the basic matrix-based attention mechanism or to develop innovative ones. The sole purpose of the attention mechanism is to make the model able to understand a context, based on the information gathered from somewhere else. For example, in image-based QA, attention layer helps the model to understand the question based on the information provided in the image such as object classes. This way, the model can realize what parts of the question are more important.<br />
#Output layer: This is the final layer of all models, generating the answer of the question based on the information provided from all the previous layers.<br />
<br />
==DCN+ structure==<br />
The DCN+ is an improvement on the previous DCN model. The overall structure of the model is the same as before. The first improvement is on the coattention module. By introducing a deep residual coattention encoder, the output of the attention layer becomes more feature-rich. The second improvement is achieved by mixing the previous cross-entropy loss with reinforcement learning rewards from self-critical policy learning. DCN+ has a decoder module that is only applicable to the SQuAD dataset since the decoder only predicts an answer span from the given context.<br />
<br />
===Deep residual coattention encoder===<br />
The previous coattention module was unable to grasp complex information based on the context and the question. Recent studies showed that stacked attention mechanisms are outperforming the single layer attention modules. In DCN+, the coattention module is stacked to make it able to self-attend to the context and grasp more information. The second modification is to use residual connectors when merging the coattention output from each layer.<br />
<br />
[[File:Coattention.png|700px|centre]]<br />
<br />
let <math>L^D \in R^{m×d}</math> and <math>L^Q \in R^{n×d}</math> denote the word embedding for the context and the question respectively. Here, <math>d, m, n</math> are the embedding vector size, document word count, and question word count respectively. The model uses a bidirectional LSTM as the contextual layer with shared wights. Also, an additional sentinel token is added at the end of the document and question to make it possible for the model to distinguish between the document and question. <math>E^D</math> and <math>E^Q</math> are outputs of the encoder(contextual) layer.<br />
<br />
\begin{align}<br />
E_1^D = BiLSTM_1(L^D) \in R^{(h×(m+1))}<br />
\end{align}<br />
\begin{align}<br />
E_1^Q = tanh(W BiLSTM_1(L^Q) \in R^{(h×(n+1))}<br />
\end{align}<br />
<br />
Here <math>h</math> is the hidden size of the LSTM. The affinity matrix is created based on the output of the encoder. The affinity matrix is the matrix that the has been used in the attention module from the introduction of attention. By performing a column-wise softmax function on the affinity matrix a vector would be generated that is a representation of the importance of each question token, based on the model's understanding of the context. Similarly, if a row-wise softmax function is applied to the affinity matrix, the output vector would represent the importance of each context word, based on the question. By multiplying these vectors to the outputs of the encoder layer, question-aware context and context-aware question representations would be created.<br />
<br />
\begin{align}<br />
A = {(E_1^D)}^T E_1^Q \in R^{(m+1)×(n+1)}<br />
\end{align}<br />
\begin{align}<br />
{S_1^D} = E_1^Q softmax(A^T) \in R^{h×(m+1)}<br />
\end{align}<br />
\begin{align}<br />
{S_1^Q} = E_1^D softmax(A) \in R^{h×(n+1)}<br />
\end{align}<br />
<br />
To make the question-aware context representation even deeper and more feature-rich. The model defines the final context representation as follows:<br />
<br />
\begin{align}<br />
{C_1^D} = S_1^Q softmax(A^T) \in R^{h×m}<br />
\end{align}<br />
<br />
Note that the model drops the dimension corresponding to the sentinel vector. The summaries also get encoded after this stage, using two bidirectional LSTMs with shared variables.<br />
<br />
\begin{align}<br />
{E_2^D} = BiLSTM_2(S_1^Q) \in R^{2h×m}<br />
\end{align}<br />
\begin{align}<br />
{E_2^Q} = BiLSTM_2(S_1^D) \in R^{2h×n}<br />
\end{align}<br />
<br />
Finally, The <math>E_1^D</math> and <E_1^Q> are the output of the coattention module. The coattention module can easily get stacked to create a deeper attention mechanism. The final output of the stacked coattention units is obtained as:<br />
<br />
\begin{align}<br />
U = BiLSTM(concat(E_1^D;E_2^D;S_1^D;S_2^D;C_1^D;C_2^D) \in R^{2h×m}<br />
\end{align}<br />
<br />
===Mixed objective using self-critical policy learning===<br />
DCN produces a distribution over that start and end positions of the answer span. Because of the dynamic nature of the decoder module, it estimates separate distributions over the start and end position of the answer dynamically.<br />
<br />
\begin{align}<br />
l_{ce}(\theta) = - \sum_{t} (log \ p_t^{start}(s|s_{t-1},e_{t-1};\theta) + log \ p_t^{end}(e|s_{t-1},e_{t-1};\theta))<br />
\end{align}<br />
<br />
In the above equation, <math>s</math> and <math>e</math> denote the respective start and end points of the ground truth answer. <math>s_t</math> and <math>e_t</math> denote the greedy estimation of the start and end positions at the <math>t</math>th decoding time step. Similarly, <math>p_t^{start} \in R^m</math> and <math>p_t^{end} \in R^m</math> denote the distribution of the start and end positions respectively. The problem with the above loss functions is that it does not consider the F1 metric for evaluation of the model. There are two metrics to estimate QA models accuracy. The first metric is the exact match and it is a binary score. If the answer string does not match with the ground truth answer even by a single character, the exact match score would be zero. The second metric is the F1 score. F1 score is basically the degree of the overlap between the predicted answer and the ground truth. <br />
For example, suppose there are more than two correct answer spans in a context, <math>A</math> and <math>B</math>, but none of the match the ground truth positions. If A has an exact string match but B does not, The cross-entropy loss would penalize both of them equally. However, if we include can F1 scores in our calculations, the loss function would penalize B and not A. To deal with this problem, DCN+ uses a self-critical reinforcement learning objective.<br />
<br />
\begin{align}<br />
l_{rl}(\theta) = -E_{\hat{\tau} \sim p_\tau} [R(s,e,\hat{s}_T,\hat{e}_T;\theta)]<br />
\end{align}<br />
<br />
\begin{align}<br />
\approx -E_{\hat{\tau} \sim p_\tau} [F_1 (ans(\hat{s}_T, \hat{e}_T), ans(s, e)) - F_1(ans(s_T, e_T), ans(s, e))]<br />
\end{align}<br />
<br />
Here <math>\hat{s} \sim p_t^{start}</math> and <math>\hat{e} \sim p_t^{end}</math> denote the sampled start and end positions respectively from the estimated distributions at <math>t</math>th decoding step. <math>\hat{\tau}</math> is a trajectory as a sequence of sampled start and end positions during all <math>T</math> decoder steps and <math>R</math> is the expected reward. Previous studies show that using a baseline for the reward reduces the variance of gradient estimates and facilitates convergence. The second term in the above equation is the baseline. DCN+ uses a self-critic that uses the F1 produced during greedy inference by the current model.<br />
<br />
[[File:loss.png|700px|centre]]<br />
<br />
==Experiments==<br />
To achieve optimal performance, the hyperparameters and training environment are fine-tuned. For tokenizing the documents, the Stanford CoreNLP reversible tokenizers has been used. For word embeddings, a pre-trained GloVE (trained on 840B common crawl) is used. The optimizer has been set to Adam and a dropout is also applied on word embeddings that zeros a word embedding with a probability of 0.075.<br />
==Results==<br />
At the time of submission, the model was able to achieve state-of-the-art results on the SQuAD, outperforming the second model on the leaderboard by 2.0% both on the exact match and F1 scores. It is worth mentioning that a 5% improvement was also achieved with respect to the original DCN model.<br />
<br />
[[File:dcn_resutls1.png|700px|centre]]<br />
<br />
In general, DCN+ was able to a achieve consistent performance improvement in almost every question category.<br />
<br />
[[File:dcn_results2.png|700px|centre]]<br />
<br />
==Refrences==</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:loss.png&diff=36944File:loss.png2018-10-20T19:55:14Z<p>Aghabuss: </p>
<hr />
<div></div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=DCN_plus:_Mixed_Objective_And_Deep_Residual_Coattention_for_Question_Answering&diff=36943DCN plus: Mixed Objective And Deep Residual Coattention for Question Answering2018-10-20T19:08:36Z<p>Aghabuss: </p>
<hr />
<div>== Introduction ==<br />
Question Answering(QA) is one of the challenging computer science tasks that need an understanding of the natural language and the ability to reason efficiently. To accurately answer the question, the model must first have a detailed understanding of the context the question is being asked from. Because the questions are usually very detailed, having a shallow knowledge from the context would lead to poor and unacceptable performance. Moreover, The model should gather all the information provided in the question and match them with its knowledge from the context. Generating the answer is another interesting task. Based on the dataset the model is meant for, the output of the model might be in a completely different form.<br />
In the past years, QA datasets have improved significantly. Previous datasets were really simple and they usually did not simulate a real-world question-answer pair. For example, Children's book test was one of the popular datasets that have been used for QA for a long time. But the real task for this dataset was to just fill empty spaces in given sentences with the appropriate words. During the past years, the importance of the QA tasks and their practical uses encouraged many to gather and crowdsource useful and more realistic datasets. The Stanford Question Answering Dataset(SQuAD), Microsoft MAchine Reading COmprehension Dataset(MS MARCO), and Visual Question Answering Dataset(VQA) are only a few examples of the currently advanced datasets.<br />
As a result of these advancements, many researchers are focusing to improve the performance of the question answering models on these datasets. Deep neural networks were able to outperform the human accuracy on a few of these datasets, but in many cases, there is still a gap between the state-of-the-art and human performance. Previously, Dynamic Coattention Networks(DCN) proved to be efficient on the SQuAD, achieving state-of-the-art performance at the time. In this work, a further modification to DCN has been done which improves the accuracy of the model by proposing a mixed objective that combines cross entropy loss with self-critical policy learning.<br />
<br />
==Overview of previous work==<br />
Most of the current QA models are made from different modules and usually stacked on top of each other. Improving one of the modules would lead to an overall performance of the model. Thus, to evaluate the efficiency of an improvement, researchers usually take a previously submitted model and replace their own improved module with the current one in the model. This is mostly because QA is an interesting discipline and has practical uses.<br />
<br />
#Embedding layer: This layer maps each word (or images in the case of visual QA) to a vector space. There are many options to choose for the embedding layer. While pre-trained GloVes or Word2Vecs showed promising results on many tasks, most models use a combination of GloVe and character level embeddings. The character level embeddings are especially useful when dealing with out-of-vocab words. In the case of dealing with images, the embeddings are usually generated using pre-trained ResNets. Using different embedding layers for images has shown to change the overall performance of the model drastically.<br />
#Contextual_layer: The purpose of this layer is to add more features to each word embedding based on the surrounding words and the context. This layer is not presented in many models including the DCN.<br />
#Attention layer: There has been a lot of investigation on the attention mechanisms in recent years. These works, mostly inspired by Bahdanau et al. (2014), try to either modify the basic matrix-based attention mechanism or to develop innovative ones. The sole purpose of the attention mechanism is to make the model able to understand a context, based on the information gathered from somewhere else. For example, in image-based QA, attention layer helps the model to understand the question based on the information provided in the image such as object classes. This way, the model can realize what parts of the question are more important.<br />
#Output layer: This is the final layer of all models, generating the answer of the question based on the information provided from all the previous layers.<br />
<br />
==DCN+ structure==<br />
The DCN+ is an improvement on the previous DCN model. The overall structure of the model is the same as before. The first improvement is on the coattention module. By introducing a deep residual coattention encoder, the output of the attention layer becomes more feature-rich. The second improvement is achieved by mixing the previous cross-entropy loss with reinforcement learning rewards from self-critical policy learning. DCN+ has a decoder module that is only applicable to the SQuAD dataset since the decoder only predicts an answer span from the given context.<br />
<br />
===Deep residual coattention encoder===<br />
The previous coattention module was unable to grasp complex information based on the context and the question. Recent studies showed that stacked attention mechanisms are outperforming the single layer attention modules. In DCN+, the coattention module is stacked to make it able to self-attend to the context and grasp more information. The second modification is to use residual connectors when merging the coattention output from each layer.<br />
<br />
[[File:Coattention.png|700px|centre]]<br />
<br />
let <math>L^D \in R^{m×d}</math> and <math>L^Q \in R^{n×d}</math> denote the word embedding for the context and the question respectively. Here, <math>d, m, n</math> are the embedding vector size, document word count, and question word count respectively. The model uses a bidirectional LSTM as the contextual layer with shared wights. Also, an additional sentinel token is added at the end of the document and question to make it possible for the model to distinguish between the document and question. <math>E^D</math> and <math>E^Q</math> are outputs of the encoder(contextual) layer.<br />
<br />
\begin{align}<br />
E_1^D = BiLSTM_1(L^D) \in R^{(h×(m+1))}<br />
\end{align}<br />
\begin{align}<br />
E_1^Q = tanh(W BiLSTM_1(L^Q) \in R^{(h×(n+1))}<br />
\end{align}<br />
<br />
Here <math>h</math> is the hidden size of the LSTM. The affinity matrix is created based on the output of the encoder. The affinity matrix is the matrix that the has been used in the attention module from the introduction of attention. By performing a column-wise softmax function on the affinity matrix a vector would be generated that is a representation of the importance of each question token, based on the model's understanding of the context. Similarly, if a row-wise softmax function is applied to the affinity matrix, the output vector would represent the importance of each context word, based on the question. By multiplying these vectors to the outputs of the encoder layer, question-aware context and context-aware question representations would be created.<br />
<br />
\begin{align}<br />
A = {(E_1^D)}^T E_1^Q \in R^{(m+1)×(n+1)}<br />
\end{align}<br />
\begin{align}<br />
{S_1^D} = E_1^Q softmax(A^T) \in R^{h×(m+1)}<br />
\end{align}<br />
\begin{align}<br />
{S_1^Q} = E_1^D softmax(A) \in R^{h×(n+1)}<br />
\end{align}<br />
<br />
To make the question-aware context representation even deeper and more feature-rich. The model defines the final context representation as follows:<br />
<br />
\begin{align}<br />
{C_1^D} = S_1^Q softmax(A^T) \in R^{h×m}<br />
\end{align}<br />
<br />
Note that the model drops the dimension corresponding to the sentinel vector. The summaries also get encoded after this stage, using two bidirectional LSTMs with shared variables.<br />
<br />
\begin{align}<br />
{E_2^D} = BiLSTM_2(S_1^Q) \in R^{2h×m}<br />
\end{align}<br />
\begin{align}<br />
{E_2^Q} = BiLSTM_2(S_1^D) \in R^{2h×n}<br />
\end{align}<br />
<br />
Finally, The <math>E_1^D</math> and <E_1^Q> are the output of the coattention module. The coattention module can easily get stacked to create a deeper attention mechanism. The final output of the stacked coattention units is obtained as:<br />
<br />
\begin{align}<br />
U = BiLSTM(concat(E_1^D;E_2^D;S_1^D;S_2^D;C_1^D;C_2^D) \in R^{2h×m}<br />
\end{align}<br />
<br />
===Mixed objective using self-critical policy learning===<br />
DCN produces a distribution over that start and end positions of the answer span. Because of the dynamic nature of the decoder module, it estimates separate distributions over the start and end position of the answer dynamically.<br />
<br />
\begin{align}<br />
l_{ce}(\theta) = - \sum_{t} (log \ p_t^{start}(s|s_{t-1},e_{t-1};\theta) + log \ p_t^{end}(e|s_{t-1},e_{t-1};\theta))<br />
\end{align}<br />
<br />
In the above equation, <math>s</math> and <math>e</math> denote the respective start and end points of the ground truth answer. <math>s_t</math> and <math>e_t</math> denote the greedy estimation of the start and end positions at the <math>t</math>th decoding time step. Similarly, <math>p_t^{start} \in R^m</math> and <math>p_t^{end} \in R^m</math> denote the distribution of the start and end positions respectively. The problem with the above loss functions is that it does not consider the F1 metric for evaluation of the model. There are two metrics to estimate QA models accuracy. The first metric is the exact match and it is a binary score. If the answer string does not match with the ground truth answer even by a single character, the exact match score would be zero. The second metric is the F1 score. F1 score is basically the degree of the overlap between the predicted answer and the ground truth. <br />
For example, suppose there are more than two correct answer spans in a context, <math>A</math> and <math>B</math>, but none of the match the ground truth positions. If A has an exact string match but B does not, The cross-entropy loss would penalize both of them equally. However, if we include can F1 scores in our calculations, the loss function would penalize B and not A. To deal with this problem, DCN+ uses a self-critical reinforcement learning objective.<br />
<br />
\begin{align}<br />
l_{rl}(\theta) = -E_{\hat{\tau} \sim p_\tau} [R(s,e,\hat{s}_T,\hat{e}_T;\theta)]<br />
\end{align}<br />
<br />
\begin{align}<br />
\approx -E_{\hat{\tau} \sim p_\tau} [F_1 (ans(\hat{s}_T, \hat{e}_T), ans(s, e)) - F_1(ans(s_T, e_T), ans(s, e))]<br />
\end{align}<br />
<br />
Here <math>\hat{s} \sim p_t^{start}</math> and <math>\hat{e} \sim p_t^{end}</math> denote the sampled start and end positions respectively from the estimated distributions at <math>t</math>th decoding step. <math>\hat{\tau}</math> is a trajectory as a sequence of sampled start and end positions during all <math>T</math> decoder steps and <math>R</math> is the expected reward. Previous studies show that using a baseline for the reward reduces the variance of gradient estimates and facilitates convergence. The second term in the above equation is the baseline. DCN+ uses a self-critic that uses the F1 produced during greedy inference by the current model.<br />
<br />
==Experiments==<br />
To achieve optimal performance, the hyperparameters and training environment are fine-tuned. For tokenizing the documents, the Stanford CoreNLP reversible tokenizers has been used. For word embeddings, a pre-trained GloVE (trained on 840B common crawl) is used. The optimizer has been set to Adam and a dropout is also applied on word embeddings that zeros a word embedding with a probability of 0.075.<br />
==Results==<br />
At the time of submission, the model was able to achieve state-of-the-art results on the SQuAD, outperforming the second model on the leaderboard by 2.0% both on the exact match and F1 scores. It is worth mentioning that a 5% improvement was also achieved with respect to the original DCN model.<br />
<br />
[[File:dcn_resutls1.png|700px|centre]]<br />
<br />
In general, DCN+ was able to a achieve consistent performance improvement in almost every question category.<br />
<br />
[[File:dcn_results2.png|700px|centre]]<br />
<br />
==Refrences==</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=DCN_plus:_Mixed_Objective_And_Deep_Residual_Coattention_for_Question_Answering&diff=36939DCN plus: Mixed Objective And Deep Residual Coattention for Question Answering2018-10-20T06:30:05Z<p>Aghabuss: </p>
<hr />
<div>== Introduction ==<br />
Question Answering(QA) is one of the challenging computer science tasks that need an understanding of the natural language and the ability to reason efficiently. To accurately answer the question, the model must first have a detailed understanding of the context the question is being asked from. Because the questions are usually very detailed, having a shallow knowledge from the context would lead to poor and unacceptable performance. Moreover, The model should gather all the information provided in the question and match them with its knowledge from the context. Generating the answer is another interesting task. Based on the dataset the model is meant for, the output of the model might be in a completely different form.<br />
In the past years, QA datasets have improved significantly. Previous datasets were really simple and they usually did not simulate a real-world question-answer pair. For example, Children's book test was one of the popular datasets that have been used for QA for a long time. But the real task for this dataset was to just fill empty spaces in given sentences with the appropriate words. During the past years, the importance of the QA tasks and their practical uses encouraged many to gather and crowdsource useful and more realistic datasets. The Stanford Question Answering Dataset(SQuAD), Microsoft MAchine Reading COmprehension Dataset(MS MARCO), and Visual Question Answering Dataset(VQA) are only a few examples of the currently advanced datasets.<br />
As a result of these advancements, many researchers are focusing to improve the performance of the question answering models on these datasets. Deep neural networks were able to outperform the human accuracy on a few of these datasets, but in many cases, there is still a gap between the state-of-the-art and human performance. Previously, Dynamic Coattention Networks(DCN) proved to be efficient on the SQuAD, achieving state-of-the-art performance at the time. In this work, a further modification to DCN has been done which improves the accuracy of the model by proposing a mixed objective that combines cross entropy loss with self-critical policy learning.<br />
<br />
==Overview of previous work==<br />
Most of the current QA models are made from different modules and usually stacked on top of each other. Improving one of the modules would lead to an overall performance of the model. Thus, to evaluate the efficiency of an improvement, researchers usually take a previously submitted model and replace their own improved module with the current one in the model. This is mostly because QA is an interesting discipline and has practical uses.<br />
<br />
#Embedding layer: This layer maps each word (or images in the case of visual QA) to a vector space. There are many options to choose for the embedding layer. While pre-trained GloVes or Word2Vecs showed promising results on many tasks, most models use a combination of GloVe and character level embeddings. The character level embeddings are especially useful when dealing with out-of-vocab words. In the case of dealing with images, the embeddings are usually generated using pre-trained ResNets. Using different embedding layers for images has shown to change the overall performance of the model drastically.<br />
#Contextual_layer: The purpose of this layer is to add more features to each word embedding based on the surrounding words and the context. This layer is not presented in many models including the DCN.<br />
#Attention layer: There has been a lot of investigation on the attention mechanisms in recent years. These works, mostly inspired by Bahdanau et al. (2014), try to either modify the basic matrix-based attention mechanism or to develop innovative ones. The sole purpose of the attention mechanism is to make the model able to understand a context, based on the information gathered from somewhere else. For example, in image-based QA, attention layer helps the model to understand the question based on the information provided in the image such as object classes. This way, the model can realize what parts of the question are more important.<br />
#Output layer: This is the final layer of all models, generating the answer of the question based on the information provided from all the previous layers.<br />
<br />
==DCN+ structure==<br />
The DCN+ is an improvement on the previous DCN model. The overall structure of the model is the same as before. The first improvement is on the coattention module. By introducing a deep residual coattention encoder, the output of the attention layer becomes more feature-rich. The second improvement is achieved by mixing the previous cross-entropy loss with reinforcement learning rewards from self-critical policy learning.<br />
<br />
#Deep residual coattention encoder:<br />
The previous coattention module was unable to grasp complex information based on the context and the question. Recent studies showed that stacked attention mechanisms are outperforming the single layer attention modules. In DCN+, the coattention module is stacked to make it able to self-attend to the context and grasp more information. The second modification is to use residual connectors when merging the coattention output from each layer.<br />
<br />
[[File:Coattention.png|700px|centre]]<br />
<br />
let <math>L^D \in R^{m×d}</math> and <math>L^Q \in R^{n×d}</math> denote the word embedding for the context and the question respectively. Here, <math>d, m, n</math> are the embedding vector size, document word count, and question word count respectively. The model uses a bidirectional LSTM as the contextual layer with shared wights. Also, an additional sentinel token is added at the end of the document and question to make it possible for the model to distinguish between the document and question. <math>E^D</math> and <math>E^Q</math> are outputs of the encoder(contextual) layer.<br />
<br />
\begin{align}<br />
E_1^D = BiLSTM_1(L^D) \in R^{(h×(m+1))}<br />
\end{align}<br />
\begin{align}<br />
E_1^Q = tanh(W BiLSTM_1(L^Q) \in R^{(h×(n+1))}<br />
\end{align}<br />
<br />
Here <math>h</math> is the hidden size of the LSTM. The affinity matrix is created based on the output of the encoder. The affinity matrix is the matrix that the has been used in the attention module from the introduction of attention. By performing a column-wise softmax function on the affinity matrix a vector would be generated that is a representation of the importance of each question token, based on the model's understanding of the context. Similarly, if a row-wise softmax function is applied to the affinity matrix, the output vector would represent the importance of each context word, based on the question. By multiplying these vectors to the outputs of the encoder layer, question-aware context and context-aware question representations would be created.<br />
<br />
\begin{align}<br />
A = {(E_1^D)}^T E_1^Q \in R^{(m+1)×(n+1)}<br />
\end{align}<br />
\begin{align}<br />
{S_1^D} = E_1^Q softmax(A^T) \in R^{h×(m+1)}<br />
\end{align}<br />
\begin{align}<br />
{S_1^Q} = E_1^D softmax(A) \in R^{h×(n+1)}<br />
\end{align}<br />
<br />
To make the question-aware context representation even deeper and more feature-rich. The model defines the final context representation as follows:<br />
<br />
\begin{align}<br />
{C_1^D} = S_1^Q softmax(A^T) \in R^{h×m}<br />
\end{align}<br />
<br />
Note that the model drops the dimension corresponding to the sentinel vector. The summaries also get encoded after this stage, using two bidirectional LSTMs with shared variables.<br />
<br />
\begin{align}<br />
{E_2^D} = BiLSTM_2(S_1^Q) \in R^{2h×m}<br />
\end{align}<br />
\begin{align}<br />
{E_2^Q} = BiLSTM_2(S_1^D) \in R^{2h×n}<br />
\end{align}<br />
<br />
Finally, The <math>E_1^D</math> and <E_1^Q> are the output of the coattention module. The coattention module can easily get stacked to create a deeper attention mechanism. The final output of the stacked coattention units is obtained as:<br />
<br />
\begin{align}<br />
U = BiLSTM(concat(E_1^D;E_2^D;S_1^D;S_2^D;C_1^D;C_2^D) \in R^{2h×m}<br />
\end{align}<br />
<br />
==Experiments==<br />
To achieve optimal performance, the hyperparameters and training environment are fine-tuned. For tokenizing the documents, the Stanford CoreNLP reversible tokenizers has been used. For word embeddings, a pre-trained GloVE (trained on 840B common crawl) is used. The optimizer has been set to Adam and a dropout is also applied on word embeddings that zeros a word embedding with a probability of 0.075.<br />
==Results==<br />
At the time of submission, the model was able to achieve state-of-the-art results on the SQuAD, outperforming the second model on the leaderboard by 2.0% both on the exact match and F1 scores. It is worth mentioning that a 5% improvement was also achieved with respect to the original DCN model.<br />
<br />
[[File:dcn_resutls1.png|700px|centre]]<br />
<br />
In general, DCN+ was able to a achieve consistent performance improvement in almost every question category.<br />
<br />
[[File:dcn_results2.png|700px|centre]]<br />
<br />
==Refrences==</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=DCN_plus:_Mixed_Objective_And_Deep_Residual_Coattention_for_Question_Answering&diff=36938DCN plus: Mixed Objective And Deep Residual Coattention for Question Answering2018-10-20T06:00:30Z<p>Aghabuss: </p>
<hr />
<div>== Introduction ==<br />
Question Answering(QA) is one of the challenging computer science tasks that need an understanding of the natural language and the ability to reason efficiently. To accurately answer the question, the model must first have a detailed understanding of the context the question is being asked from. Because the questions are usually very detailed, having a shallow knowledge from the context would lead to poor and unacceptable performance. Moreover, The model should gather all the information provided in the question and match them with its knowledge from the context. Generating the answer is another interesting task. Based on the dataset the model is meant for, the output of the model might be in a completely different form.<br />
In the past years, QA datasets have improved significantly. Previous datasets were really simple and they usually did not simulate a real-world question-answer pair. For example, Children's book test was one of the popular datasets that have been used for QA for a long time. But the real task for this dataset was to just fill empty spaces in given sentences with the appropriate words. During the past years, the importance of the QA tasks and their practical uses encouraged many to gather and crowdsource useful and more realistic datasets. The Stanford Question Answering Dataset(SQuAD), Microsoft MAchine Reading COmprehension Dataset(MS MARCO), and Visual Question Answering Dataset(VQA) are only a few examples of the currently advanced datasets.<br />
As a result of these advancements, many researchers are focusing to improve the performance of the question answering models on these datasets. Deep neural networks were able to outperform the human accuracy on a few of these datasets, but in many cases, there is still a gap between the state-of-the-art and human performance. Previously, Dynamic Coattention Networks(DCN) proved to be efficient on the SQuAD, achieving state-of-the-art performance at the time. In this work, a further modification to DCN has been done which improves the accuracy of the model by proposing a mixed objective that combines cross entropy loss with self-critical policy learning.<br />
<br />
==Overview of previous work==<br />
Most of the current QA models are made from different modules and usually stacked on top of each other. Improving one of the modules would lead to an overall performance of the model. Thus, to evaluate the efficiency of an improvement, researchers usually take a previously submitted model and replace their own improved module with the current one in the model. This is mostly because QA is an interesting discipline and has practical uses.<br />
<br />
#Embedding layer: This layer maps each word (or images in the case of visual QA) to a vector space. There are many options to choose for the embedding layer. While pre-trained GloVes or Word2Vecs showed promising results on many tasks, most models use a combination of GloVe and character level embeddings. The character level embeddings are especially useful when dealing with out-of-vocab words. In the case of dealing with images, the embeddings are usually generated using pre-trained ResNets. Using different embedding layers for images has shown to change the overall performance of the model drastically.<br />
#Contextual_layer: The purpose of this layer is to add more features to each word embedding based on the surrounding words and the context. This layer is not presented in many models including the DCN.<br />
#Attention layer: There has been a lot of investigation on the attention mechanisms in recent years. These works, mostly inspired by Bahdanau et al. (2014), try to either modify the basic matrix-based attention mechanism or to develop innovative ones. The sole purpose of the attention mechanism is to make the model able to understand a context, based on the information gathered from somewhere else. For example, in image-based QA, attention layer helps the model to understand the question based on the information provided in the image such as object classes. This way, the model can realize what parts of the question are more important.<br />
#Output layer: This is the final layer of all models, generating the answer of the question based on the information provided from all the previous layers.<br />
<br />
==DCN+ structure==<br />
The DCN+ is an improvement on the previous DCN model. The overall structure of the model is the same as before. The first improvement is on the co-attention module. By introducing a deep residual co-attention encoder, the output of the attention layer becomes more feature-rich. The second improvement is achieved by mixing the previous cross-entropy loss with reinforcement learning rewards from self-critical policy learning.<br />
<br />
#Deep residual co-attention encoder:<br />
The previous co-attention module was unable to grasp complex information based on the context and the question. Recent studies showed that stacked attention mechanisms are outperforming the single layer attention modules. In DCN+, the co-attention module is stacked to make it able to self-attend to the context and grasp more information. The second modification is to use residual connectors when merging the co-attention output from each layer.<br />
<br />
[[File:Coattention.png|700px|centre]]<br />
<br />
let <math>L^D \in R^{m×d}</math> and <math>L^Q \in R^{n×d}</math> denote the word embedding for the context and the question respectively. Here, <math>d, m, n</math> are the embedding vector size, document word count, and question word count respectively. The model uses a bidirectional LSTM as the contextual layer with shared wights. Also, an additional sentinel token is added at the end of the document and question to make it possible for the model to distinguish between the document and question. <math>E^D</math> and <math>E^Q</math> are outputs of the encoder(contextual) layer.<br />
<br />
\begin{align}<br />
E_1^D = BiLSTM_1(L^D) \in R^{(h×(m+1))}<br />
\end{align}<br />
\begin{align}<br />
E_1^Q = tanh(W BiLSTM_1(L^Q) \in R^{(h×(n+1))}<br />
\end{align}<br />
<br />
Here <math>h</math> is the hidden size of the LSTM. The affinity matrix is created based on the output of the encoder. The affinity matrix is the matrix that the has been used in the attention module from the introduction of attention. By performing a column-wise softmax function on the affinity matrix a vector would be generated that is a representation of the importance of each question token, based on the model's understanding of the context. Similarly, if a row-wise softmax function is applied to the affinity matrix, the output vector would represent the importance of each context word, based on the question. By multiplying these vectors to the outputs of the encoder layer, question-aware context and context-aware question representations would be created.<br />
<br />
\begin{align}<br />
A = {(E_1^D)}^T E_1^Q \in R^{(m+1)×(n+1)}<br />
\end{align}<br />
\begin{align}<br />
{S_1^D} = E_1^Q softmax(A^T) \in R^{h×(m+1}<br />
\end{align}<br />
\begin{align}<br />
{S_1^Q} = E_1^D softmax(A) \in R^{h×(n+1}<br />
\end{align}<br />
<br />
==Experiments==<br />
To achieve optimal performance, the hyperparameters and training environment are fine-tuned. For tokenizing the documents, the Stanford CoreNLP reversible tokenizers has been used. For word embeddings, a pre-trained GloVE (trained on 840B common crawl) is used. The optimizer has been set to Adam and a dropout is also applied on word embeddings that zeros a word embedding with a probability of 0.075.<br />
==Results==<br />
At the time of submission, the model was able to achieve state-of-the-art results on the SQuAD, outperforming the second model on the leaderboard by 2.0% both on the exact match and F1 scores. It is worth mentioning that a 5% improvement was also achieved with respect to the original DCN model.<br />
<br />
[[File:dcn_resutls1.png|700px|centre]]<br />
<br />
In general, DCN+ was able to a achieve consistent performance improvement in almost evey question category.<br />
<br />
[[File:dcn_results2.png|700px|centre]]<br />
<br />
==Refrences==</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=DCN_plus:_Mixed_Objective_And_Deep_Residual_Coattention_for_Question_Answering&diff=36937DCN plus: Mixed Objective And Deep Residual Coattention for Question Answering2018-10-20T05:33:05Z<p>Aghabuss: Created page with "== Introduction == Question Answering(QA) is one of the challenging computer science tasks that need an understanding of the natural language and the ability to reason efficie..."</p>
<hr />
<div>== Introduction ==<br />
Question Answering(QA) is one of the challenging computer science tasks that need an understanding of the natural language and the ability to reason efficiently. To accurately answer the question, the model must first have a detailed understanding of the context the question is being asked from. Because the questions are usually very detailed, having a shallow knowledge from the context would lead to poor and unacceptable performance. Moreover, The model should gather all the information provided in the question and match them with its knowledge from the context. Generating the answer is another interesting task. Based on the dataset the model is meant for, the output of the model might be in a completely different form.<br />
In the past years, QA datasets have improved significantly. Previous datasets were really simple and they usually did not simulate a real-world question-answer pair. For example, Children's book test was one of the popular datasets that have been used for QA for a long time. But the real task for this dataset was to just fill empty spaces in given sentences with the appropriate words. During the past years, the importance of the QA tasks and their practical uses encouraged many to gather and crowdsource useful and more realistic datasets. The Stanford Question Answering Dataset(SQuAD), Microsoft MAchine Reading COmprehension Dataset(MS MARCO), and Visual Question Answering Dataset(VQA) are only a few examples of the currently advanced datasets.<br />
As a result of these advancements, many researchers are focusing to improve the performance of the question answering models on these datasets. Deep neural networks were able to outperform the human accuracy on a few of these datasets, but in many cases, there is still a gap between the state-of-the-art and human performance. Previously, Dynamic Coattention Networks(DCN) proved to be efficient on the SQuAD, achieving state-of-the-art performance at the time. In this work, a further modification to DCN has been done which improves the accuracy of the model by proposing a mixed objective that combines cross entropy loss with self-critical policy learning.<br />
<br />
==Overview of previous work==<br />
Most of the current QA models are made from different modules and usually stacked on top of each other. Improving one of the modules would lead to an overall performance of the model. Thus, to evaluate the efficiency of an improvement, researchers usually take a previously submitted model and replace their own improved module with the current one in the model. This is mostly because QA is an interesting discipline and has practical uses.<br />
<br />
#Embedding layer: This layer maps each word (or images in the case of visual QA) to a vector space. There are many options to choose for the embedding layer. While pre-trained GloVes or Word2Vecs showed promising results on many tasks, most models use a combination of GloVe and character level embeddings. The character level embeddings are especially useful when dealing with out-of-vocab words. In the case of dealing with images, the embeddings are usually generated using pre-trained ResNets. Using different embedding layers for images has shown to change the overall performance of the model drastically.<br />
#Contextual_layer: The purpose of this layer is to add more features to each word embedding based on the surrounding words and the context. This layer is not presented in many models including the DCN.<br />
#Attention layer: There has been a lot of investigation on the attention mechanisms in recent years. These works, mostly inspired by Bahdanau et al. (2014), try to either modify the basic matrix-based attention mechanism or to develop innovative ones. The sole purpose of the attention mechanism is to make the model able to understand a context, based on the information gathered from somewhere else. For example, in image-based QA, attention layer helps the model to understand the question based on the information provided in the image such as object classes. This way, the model can realize what parts of the question are more important.<br />
#Output layer: This is the final layer of all models, generating the answer of the question based on the information provided from all the previous layers.<br />
<br />
==DCN+ structure==<br />
The DCN+ is an improvement on the previous DCN model. The overall structure of the model is the same as before. The first improvement is on the co-attention module. By introducing a deep residual co-attention encoder, the output of the attention layer becomes more feature-rich. The second improvement is achieved by mixing the previous cross-entropy loss with reinforcement learning rewards from self-critical policy learning.<br />
<br />
#Deep residual co-attention encoder:<br />
The previous co-attention module was unable to grasp complex information based on the context and the question. Recent studies showed that stacked attention mechanisms are outperforming the single layer attention modules. In DCN+, the co-attention module is stacked to make it able to self-attend to the context and grasp more information. The second modification is to use residual connectors when merging the co-attention output from each layer.<br />
<br />
[[File:Coattention.png|700px|centre]]<br />
<br />
let <math>L^D \in R^{m×d}</math> and <math>L^Q \in R^{n×d}</math> denote the word embedding for the context and the question respectively. Here, <math>d, m, n</math> are the embedding vector size, document word count, and question word count respectively. The model uses a bidirectional LSTM as the contextual layer with shared wights. Also, an additional sentinel token is added at the end of the document and question to make it possible for the model to distinguish between the document and question. <math>E^D</math> and <math>E^Q</math> are outputs of the encoder(contextual) layer.<br />
<br />
\begin{align}<br />
E^D = BiLSTM(L^D) \in R^(h×(m+1))<br />
\end{align}<br />
\begin{align}<br />
E^Q = tanh(W BiLSTM(L^Q) \in R^(h×(n+1))<br />
\end{align}<br />
<br />
Here <math>h</math> is the hidden size of the LSTM. The affinity matrix is created based on the output of the encoder. The affinity matrix is the matrix that the attention module is created based on it. Then <br />
<br />
==Experiments==<br />
To achieve optimal performance, the hyperparameters and training environment are fine-tuned. For tokenizing the documents, the Stanford CoreNLP reversible tokenizers has been used. For word embeddings, a pre-trained GloVE (trained on 840B common crawl) is used. The optimizer has been set to Adam and a dropout is also applied on word embeddings that zeros a word embedding with a probability of 0.075.<br />
==Results==<br />
At the time of submission, the model was able to achieve state-of-the-art results on the SQuAD, outperforming the second model on the leaderboard by 2.0% both on the exact match and F1 scores. It is worth mentioning that a 5% improvement was also achieved with respect to the original DCN model.<br />
<br />
[[File:dcn_resutls1.png|700px|centre]]<br />
<br />
In general, DCN+ was able to a achieve consistent performance improvement in almost evey question category.<br />
<br />
[[File:dcn_results2.png|700px|centre]]<br />
<br />
==Refrences==</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:dcn_results2.png&diff=36936File:dcn results2.png2018-10-20T05:27:58Z<p>Aghabuss: </p>
<hr />
<div></div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:dcn_resutls1.png&diff=36935File:dcn resutls1.png2018-10-20T05:10:48Z<p>Aghabuss: </p>
<hr />
<div></div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:Coattention.png&diff=36933File:Coattention.png2018-10-20T03:27:25Z<p>Aghabuss: </p>
<hr />
<div></div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946F18&diff=36921stat946F182018-10-19T22:59:25Z<p>Aghabuss: /* Paper presentation */</p>
<hr />
<div>== [[F18-STAT946-Proposal| Project Proposal ]] ==<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="700pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|-<br />
|Feb 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946w18/Unsupervised_Machine_Translation_Using_Monolingual_Corpora_Only Summary]]<br />
|-<br />
|Oct 25 || Dhruv Kumar || 1 || Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs || [https://openreview.net/pdf?id=rkRwGg-0Z Paper] || <br />
[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946F18/Beyond_Word_Importance_Contextual_Decomposition_to_Extract_Interactions_from_LSTMs Summary]<br />
|-<br />
|Oct 25 || Amirpasha Ghabussi || 2 || DCN+: Mixed Objective And Deep Residual Coattention for Question Answering || [https://openreview.net/pdf?id=H1meywxRW Paper] ||<br />
[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=DCN_plus:_Mixed_Objective_And_Deep_Residual_Coattention_for_Question_Answering Summary]<br />
|-<br />
|Oct 25 || Juan Carrillo || 3 || Hierarchical Representations for Efficient Architecture Search || [https://arxiv.org/abs/1711.00436 Paper] || <br />
[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946F18/Hierarchical_Representations_for_Efficient_Architecture_Search Summary]<br />
|-<br />
|Oct 30 || Manpreet Singh Minhas || 1|| End-to-end Active Object Tracking via Reinforcement Learning || [http://proceedings.mlr.press/v80/luo18a/luo18a.pdf Paper] || <br />
|-<br />
|Oct 30 || Marvin Pafla || 2 || Fairness Without Demographics in Repeated Loss Minimization || [http://proceedings.mlr.press/v80/hashimoto18a.html Paper] || <br />
|-<br />
|Oct 30 || Glen Chalatov || 3 || Pixels to Graphs by Associative Embedding || [http://papers.nips.cc/paper/6812-pixels-to-graphs-by-associative-embedding Paper] ||<br />
|-<br />
|Nov 1 || Sriram Ganapathi Subramanian || 1||Differentiable plasticity: training plastic neural networks with backpropagation || [http://proceedings.mlr.press/v80/miconi18a.html Paper] ||<br />
|-<br />
|Nov 1 || Hadi Nekoei || 1|| Synthesizing Programs for Images using Reinforced Adversarial Learning || [http://proceedings.mlr.press/v80/ganin18a.html Paper] || <br />
|-<br />
|Nov 1 || Henry Chen || 1|| DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks || [https://ieeexplore.ieee.org/abstract/document/7989236 Paper] || <br />
|-<br />
|Nov 6 || Nargess Heydari || 2 || || || <br />
|-<br />
|Nov 6 || Aravind Ravi || 3 || Towards Image Understanding from Deep Compression Without Decoding || [https://openreview.net/forum?id=HkXWCMbRW Paper] || <br />
|-<br />
|Nov 6 || Ronald Feng || 1 || Unsupervised Representation Learning by Predicting Image Rotations || [https://openreview.net/pdf?id=S1v4N2l0- Paper] || <br />
|-<br />
|Nov 8 || Neel Bhatt || 1 || Annotating Object Instances with a Polygon-RNN || [https://www.cs.utoronto.ca/~fidler/papers/paper_polyrnn.pdf Paper] || <br />
|-<br />
|Nov 8 || Jacob Manuel || 2 || || || <br />
|-<br />
|Nov 8 || Charupriya Sharma|| 2 || || || <br />
|-<br />
|NOv 13 || Sagar Rajendran || 1|| Zero-Shot Visual Imitation || [https://openreview.net/pdf?id=BkisuzWRW Paper] || <br />
|-<br />
|Nov 13 || Jiazhen Chen || 2|| || || <br />
|-<br />
|Nov 13 || Neil Budnarain || 2|| PixelNN: Example-Based Image Synthesis || [https://openreview.net/pdf?id=Syhr6pxCW Paper] || <br />
|-<br />
|NOv 15 || Zheng Ma || 3|| Reinforcement Learning of Theorem Proving || [https://arxiv.org/abs/1805.07563 Paper] || <br />
|-<br />
|Nov 15 || Abdul Khader Naik || 4|| || ||<br />
|-<br />
|Nov 15 || Johra Muhammad Moosa || 2|| Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin || [https://papers.nips.cc/paper/7255-attend-and-predict-understanding-gene-regulation-by-selective-attention-on-chromatin.pdf Paper] || <br />
|-<br />
|NOv 20 || Zahra Rezapour Siahgourabi || 1|| || || <br />
|-<br />
|Nov 20 || Shubham Koundinya || 6|| || || <br />
|-<br />
|Nov 20 || Salman Khan || || Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples || [http://proceedings.mlr.press/v80/athalye18a.html paper] || <br />
|-<br />
|NOv 22 ||Soroush Ameli || 3|| Learning to Navigate in Cities Without a Map || [https://arxiv.org/abs/1804.00168 paper] || <br />
|-<br />
|Nov 22 ||Ivan Li || 23 || Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate || [https://arxiv.org/pdf/1806.05161v2.pdf Paper] ||<br />
|-<br />
|Nov 22 ||Sigeng Chen || 2 || || ||<br />
|-<br />
|Nov 27 || Aileen Li || 8|| Spatially Transformed Adversarial Examples ||[https://openreview.net/pdf?id=HyydRMZC- Paper] || <br />
|-<br />
|NOv 27 ||Xudong Peng || 9|| Multi-Scale Dense Networks for Resource Efficient Image Classification || [https://openreview.net/pdf?id=Hk2aImxAb Paper] || <br />
|-<br />
|Nov 27 ||Xinyue Zhang || 10|| An Inference-Based Policy Gradient Method for Learning Options || [http://proceedings.mlr.press/v80/smith18a/smith18a.pdf Paper] || <br />
|-<br />
|NOv 29 ||Junyi Zhang || 11|| || || <br />
|-<br />
|Nov 29 ||Travis Bender || 12|| Automatic Goal Generation for Reinforcement Learning Agents || [http://proceedings.mlr.press/v80/florensa18a/florensa18a.pdf Paper] ||<br />
|-<br />
|Nov 29 ||Patrick Li || 12|| Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices || [https://www.cse.ust.hk/~huangzf/ICML18.pdf Paper] ||<br />
|-<br />
|Makup || Ruijie Zhang || 1 || Searching for Efficient Multi-Scale Architectures for Dense Image Prediction || [https://arxiv.org/pdf/1809.04184.pdf Paper]||<br />
|-<br />
|Makup || Ahmed Afify || 2||Don't Decay the Learning Rate, Increase the Batch Size || [https://openreview.net/pdf?id=B1Yy1BxCZ Paper]||<br />
|-<br />
|Makup || Gaurav Sahu || 3 || TBD || ||<br />
|-<br />
|Makup || Kashif Khan || 4 || Wasserstein Auto-Encoders || [https://arxiv.org/pdf/1711.01558.pdf Paper] ||<br />
|-<br />
|Makup || Shala Chen || || A NEURAL REPRESENTATION OF SKETCH DRAWINGS || ||<br />
|-<br />
|Makup || Ki Beom Lee || || || ||<br />
|-<br />
|Makup || Wesley Fisher || || Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling || [http://proceedings.mlr.press/v80/lee18b/lee18b.pdf Paper] ||</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946F18&diff=36920stat946F182018-10-19T22:58:01Z<p>Aghabuss: /* Paper presentation */</p>
<hr />
<div>== [[F18-STAT946-Proposal| Project Proposal ]] ==<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="700pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|-<br />
|Feb 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946w18/Unsupervised_Machine_Translation_Using_Monolingual_Corpora_Only Summary]]<br />
|-<br />
|Oct 25 || Dhruv Kumar || 1 || Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs || [https://openreview.net/pdf?id=rkRwGg-0Z Paper] || <br />
[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946F18/Beyond_Word_Importance_Contextual_Decomposition_to_Extract_Interactions_from_LSTMs Summary]<br />
|-<br />
|Oct 25 || Amirpasha Ghabussi || 2 || DCN+: Mixed Objective And Deep Residual Coattention for Question Answering || [https://openreview.net/pdf?id=H1meywxRW Paper] ||<br />
[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=DCN+:_Mixed_Objective_And_Deep_Residual_Coattention_for_Question_Answering Summary]<br />
|-<br />
|Oct 25 || Juan Carrillo || 3 || Hierarchical Representations for Efficient Architecture Search || [https://arxiv.org/abs/1711.00436 Paper] || <br />
[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946F18/Hierarchical_Representations_for_Efficient_Architecture_Search Summary]<br />
|-<br />
|Oct 30 || Manpreet Singh Minhas || 1|| End-to-end Active Object Tracking via Reinforcement Learning || [http://proceedings.mlr.press/v80/luo18a/luo18a.pdf Paper] || <br />
|-<br />
|Oct 30 || Marvin Pafla || 2 || Fairness Without Demographics in Repeated Loss Minimization || [http://proceedings.mlr.press/v80/hashimoto18a.html Paper] || <br />
|-<br />
|Oct 30 || Glen Chalatov || 3 || Pixels to Graphs by Associative Embedding || [http://papers.nips.cc/paper/6812-pixels-to-graphs-by-associative-embedding Paper] ||<br />
|-<br />
|Nov 1 || Sriram Ganapathi Subramanian || 1||Differentiable plasticity: training plastic neural networks with backpropagation || [http://proceedings.mlr.press/v80/miconi18a.html Paper] ||<br />
|-<br />
|Nov 1 || Hadi Nekoei || 1|| Synthesizing Programs for Images using Reinforced Adversarial Learning || [http://proceedings.mlr.press/v80/ganin18a.html Paper] || <br />
|-<br />
|Nov 1 || Henry Chen || 1|| DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks || [https://ieeexplore.ieee.org/abstract/document/7989236 Paper] || <br />
|-<br />
|Nov 6 || Nargess Heydari || 2 || || || <br />
|-<br />
|Nov 6 || Aravind Ravi || 3 || Towards Image Understanding from Deep Compression Without Decoding || [https://openreview.net/forum?id=HkXWCMbRW Paper] || <br />
|-<br />
|Nov 6 || Ronald Feng || 1 || Unsupervised Representation Learning by Predicting Image Rotations || [https://openreview.net/pdf?id=S1v4N2l0- Paper] || <br />
|-<br />
|Nov 8 || Neel Bhatt || 1 || Annotating Object Instances with a Polygon-RNN || [https://www.cs.utoronto.ca/~fidler/papers/paper_polyrnn.pdf Paper] || <br />
|-<br />
|Nov 8 || Jacob Manuel || 2 || || || <br />
|-<br />
|Nov 8 || Charupriya Sharma|| 2 || || || <br />
|-<br />
|NOv 13 || Sagar Rajendran || 1|| Zero-Shot Visual Imitation || [https://openreview.net/pdf?id=BkisuzWRW Paper] || <br />
|-<br />
|Nov 13 || Jiazhen Chen || 2|| || || <br />
|-<br />
|Nov 13 || Neil Budnarain || 2|| PixelNN: Example-Based Image Synthesis || [https://openreview.net/pdf?id=Syhr6pxCW Paper] || <br />
|-<br />
|NOv 15 || Zheng Ma || 3|| Reinforcement Learning of Theorem Proving || [https://arxiv.org/abs/1805.07563 Paper] || <br />
|-<br />
|Nov 15 || Abdul Khader Naik || 4|| || ||<br />
|-<br />
|Nov 15 || Johra Muhammad Moosa || 2|| Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin || [https://papers.nips.cc/paper/7255-attend-and-predict-understanding-gene-regulation-by-selective-attention-on-chromatin.pdf Paper] || <br />
|-<br />
|NOv 20 || Zahra Rezapour Siahgourabi || 1|| || || <br />
|-<br />
|Nov 20 || Shubham Koundinya || 6|| || || <br />
|-<br />
|Nov 20 || Salman Khan || || Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples || [http://proceedings.mlr.press/v80/athalye18a.html paper] || <br />
|-<br />
|NOv 22 ||Soroush Ameli || 3|| Learning to Navigate in Cities Without a Map || [https://arxiv.org/abs/1804.00168 paper] || <br />
|-<br />
|Nov 22 ||Ivan Li || 23 || Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate || [https://arxiv.org/pdf/1806.05161v2.pdf Paper] ||<br />
|-<br />
|Nov 22 ||Sigeng Chen || 2 || || ||<br />
|-<br />
|Nov 27 || Aileen Li || 8|| Spatially Transformed Adversarial Examples ||[https://openreview.net/pdf?id=HyydRMZC- Paper] || <br />
|-<br />
|NOv 27 ||Xudong Peng || 9|| Multi-Scale Dense Networks for Resource Efficient Image Classification || [https://openreview.net/pdf?id=Hk2aImxAb Paper] || <br />
|-<br />
|Nov 27 ||Xinyue Zhang || 10|| An Inference-Based Policy Gradient Method for Learning Options || [http://proceedings.mlr.press/v80/smith18a/smith18a.pdf Paper] || <br />
|-<br />
|NOv 29 ||Junyi Zhang || 11|| || || <br />
|-<br />
|Nov 29 ||Travis Bender || 12|| Automatic Goal Generation for Reinforcement Learning Agents || [http://proceedings.mlr.press/v80/florensa18a/florensa18a.pdf Paper] ||<br />
|-<br />
|Nov 29 ||Patrick Li || 12|| Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices || [https://www.cse.ust.hk/~huangzf/ICML18.pdf Paper] ||<br />
|-<br />
|Makup || Ruijie Zhang || 1 || Searching for Efficient Multi-Scale Architectures for Dense Image Prediction || [https://arxiv.org/pdf/1809.04184.pdf Paper]||<br />
|-<br />
|Makup || Ahmed Afify || 2||Don't Decay the Learning Rate, Increase the Batch Size || [https://openreview.net/pdf?id=B1Yy1BxCZ Paper]||<br />
|-<br />
|Makup || Gaurav Sahu || 3 || TBD || ||<br />
|-<br />
|Makup || Kashif Khan || 4 || Wasserstein Auto-Encoders || [https://arxiv.org/pdf/1711.01558.pdf Paper] ||<br />
|-<br />
|Makup || Shala Chen || || A NEURAL REPRESENTATION OF SKETCH DRAWINGS || ||<br />
|-<br />
|Makup || Ki Beom Lee || || || ||<br />
|-<br />
|Makup || Wesley Fisher || || Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling || [http://proceedings.mlr.press/v80/lee18b/lee18b.pdf Paper] ||</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946F18&diff=36917stat946F182018-10-19T22:42:47Z<p>Aghabuss: /* Paper presentation */</p>
<hr />
<div>== [[F18-STAT946-Proposal| Project Proposal ]] ==<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="700pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|-<br />
|Feb 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946w18/Unsupervised_Machine_Translation_Using_Monolingual_Corpora_Only Summary]]<br />
|-<br />
|Oct 25 || Dhruv Kumar || 1 || Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs || [https://openreview.net/pdf?id=rkRwGg-0Z Paper] || <br />
[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946F18/Beyond_Word_Importance_Contextual_Decomposition_to_Extract_Interactions_from_LSTMs Summary]<br />
|-<br />
|Oct 25 || Amirpasha Ghabussi || 2 || DCN+: Mixed Objective And Deep Residual Coattention for Question Answering || [https://openreview.net/pdf?id=H1meywxRW Paper] ||<br />
|-<br />
|Oct 25 || Juan Carrillo || 3 || Hierarchical Representations for Efficient Architecture Search || [https://arxiv.org/abs/1711.00436 Paper] || <br />
[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946F18/Hierarchical_Representations_for_Efficient_Architecture_Search Summary]<br />
|-<br />
|Oct 30 || Manpreet Singh Minhas || 1|| End-to-end Active Object Tracking via Reinforcement Learning || [http://proceedings.mlr.press/v80/luo18a/luo18a.pdf Paper] || <br />
|-<br />
|Oct 30 || Marvin Pafla || 2 || Fairness Without Demographics in Repeated Loss Minimization || [http://proceedings.mlr.press/v80/hashimoto18a.html Paper] || <br />
|-<br />
|Oct 30 || Glen Chalatov || 3 || Pixels to Graphs by Associative Embedding || [http://papers.nips.cc/paper/6812-pixels-to-graphs-by-associative-embedding Paper] ||<br />
|-<br />
|Nov 1 || Sriram Ganapathi Subramanian || 1||Differentiable plasticity: training plastic neural networks with backpropagation || [http://proceedings.mlr.press/v80/miconi18a.html Paper] ||<br />
|-<br />
|Nov 1 || Hadi Nekoei || 1|| Synthesizing Programs for Images using Reinforced Adversarial Learning || [http://proceedings.mlr.press/v80/ganin18a.html Paper] || <br />
|-<br />
|Nov 1 || Henry Chen || 1|| DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks || [https://ieeexplore.ieee.org/abstract/document/7989236 Paper] || <br />
|-<br />
|Nov 6 || Nargess Heydari || 2 || || || <br />
|-<br />
|Nov 6 || Aravind Ravi || 3 || Towards Image Understanding from Deep Compression Without Decoding || [https://openreview.net/forum?id=HkXWCMbRW Paper] || <br />
|-<br />
|Nov 6 || Ronald Feng || || || || <br />
|-<br />
|Nov 8 || Neel Bhatt || 1 || Annotating Object Instances with a Polygon-RNN || [https://www.cs.utoronto.ca/~fidler/papers/paper_polyrnn.pdf Paper] || <br />
|-<br />
|Nov 8 || Jacob Manuel || 2 || || || <br />
|-<br />
|Nov 8 || Charupriya Sharma|| 2 || || || <br />
|-<br />
|NOv 13 || Sagar Rajendran || 1|| Zero-Shot Visual Imitation || [https://openreview.net/pdf?id=BkisuzWRW Paper] || <br />
|-<br />
|Nov 13 || Jiazhen Chen || 2|| || || <br />
|-<br />
|Nov 13 || Neil Budnarain || 2|| PixelNN: Example-Based Image Synthesis || [https://openreview.net/pdf?id=Syhr6pxCW Paper] || <br />
|-<br />
|NOv 15 || Zheng Ma || 3|| Reinforcement Learning of Theorem Proving || [https://arxiv.org/abs/1805.07563 Paper] || <br />
|-<br />
|Nov 15 || Abdul Khader Naik || 4|| || ||<br />
|-<br />
|Nov 15 || Johra Muhammad Moosa || 2|| Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin || [https://papers.nips.cc/paper/7255-attend-and-predict-understanding-gene-regulation-by-selective-attention-on-chromatin.pdf Paper] || <br />
|-<br />
|NOv 20 || Zahra Rezapour Siahgourabi || 1|| || || <br />
|-<br />
|Nov 20 || Shubham Koundinya || 6|| || || <br />
|-<br />
|Nov 20 || Salman Khan || || Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples || [http://proceedings.mlr.press/v80/athalye18a.html paper] || <br />
|-<br />
|NOv 22 ||Soroush Ameli || 3|| Learning to Navigate in Cities Without a Map || [https://arxiv.org/abs/1804.00168 paper] || <br />
|-<br />
|Nov 22 ||Ivan Li || 23 || Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate || [https://arxiv.org/pdf/1806.05161v2.pdf Paper] ||<br />
|-<br />
|Nov 22 ||Sigeng Chen || 2 || || ||<br />
|-<br />
|Nov 27 || Aileen Li || 8|| Spatially Transformed Adversarial Examples ||[https://openreview.net/pdf?id=HyydRMZC- Paper] || <br />
|-<br />
|NOv 27 ||Xudong Peng || 9|| Multi-Scale Dense Networks for Resource Efficient Image Classification || [https://openreview.net/pdf?id=Hk2aImxAb Paper] || <br />
|-<br />
|Nov 27 ||Xinyue Zhang || 10|| An Inference-Based Policy Gradient Method for Learning Options || [http://proceedings.mlr.press/v80/smith18a/smith18a.pdf Paper] || <br />
|-<br />
|NOv 29 ||Junyi Zhang || 11|| || || <br />
|-<br />
|Nov 29 ||Travis Bender || 12|| Automatic Goal Generation for Reinforcement Learning Agents || [http://proceedings.mlr.press/v80/florensa18a/florensa18a.pdf Paper] ||<br />
|-<br />
|Nov 29 ||Patrick Li || 12|| Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices || [https://www.cse.ust.hk/~huangzf/ICML18.pdf Paper] ||<br />
|-<br />
|Makup || Ruijie Zhang || 1 || Searching for Efficient Multi-Scale Architectures for Dense Image Prediction || [https://arxiv.org/pdf/1809.04184.pdf Paper]||<br />
|-<br />
|Makup || Ahmed Afify || 2||Don't Decay the Learning Rate, Increase the Batch Size || [https://openreview.net/pdf?id=B1Yy1BxCZ Paper]||<br />
|-<br />
|Makup || Gaurav Sahu || 3 || TBD || ||<br />
|-<br />
|Makup || Kashif Khan || 4 || Wasserstein Auto-Encoders || [https://arxiv.org/pdf/1711.01558.pdf Paper] ||<br />
|-<br />
|Makup || Shala Chen || || A NEURAL REPRESENTATION OF SKETCH DRAWINGS || ||<br />
|-<br />
|Makup || Ki Beom Lee || || || ||<br />
|-<br />
|Makup || Wesley Fisher || || Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling || [http://proceedings.mlr.press/v80/lee18b/lee18b.pdf Paper] ||</div>Aghabusshttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946F18&diff=36739stat946F182018-10-16T15:44:32Z<p>Aghabuss: /* Paper presentation */</p>
<hr />
<div>== [[F18-STAT946-Proposal| Project Proposal ]] ==<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="700pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|-<br />
|Feb 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [http://wikicoursenote.com/wiki/Stat946f15/Sequence_to_sequence_learning_with_neural_networks#Long_Short-Term_Memory_Recurrent_Neural_Network Summary]<br />
|-<br />
|Oct 25 || Dhruv Kumar || 1 || Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs || [https://openreview.net/pdf?id=rkRwGg-0Z Paper] || <br />
|-<br />
|Oct 25 || Amirpasha Ghabussi || 2 || || ||<br />
|-<br />
|Oct 25 || Juan Carrillo || 3 || Hierarchical Representations for Efficient Architecture Search || [https://arxiv.org/abs/1711.00436 Paper] || <br />
|-<br />
|Oct 30 || Manpreet Singh Minhas || 1|| || || <br />
|-<br />
|Oct 30 || Marvin Pafla || 2 || Fairness Without Demographics in Repeated Loss Minimization || [http://proceedings.mlr.press/v80/hashimoto18a.html Paper] || <br />
|-<br />
|Oct 30 || Glen Chalatov || 3 || TBD || ||<br />
|-<br />
|Nov 1 || Sriram Ganapathi Subramanian || 1||Differentiable plasticity: training plastic neural networks with backpropagation || [http://proceedings.mlr.press/v80/miconi18a.html Paper] <br />
|-<br />
|Nov 1 || Hadi Nekoei || 1|| Synthesizing Programs for Images using Reinforced Adversarial Learning || [http://proceedings.mlr.press/v80/ganin18a.html Paper] || <br />
|-<br />
|Nov 1 || Henry Chen || 1|| DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks || [https://ieeexplore.ieee.org/abstract/document/7989236 Paper] || <br />
|-<br />
|Nov 6 || Nargess Heydari || 2 || || || <br />
|-<br />
|Nov 6 || Aravind Ravi || 3 || Towards Image Understanding from Deep Compression Without Decoding || [https://openreview.net/forum?id=HkXWCMbRW Paper] || <br />
|-<br />
|Nov 6 || Ronald Feng || || || || <br />
|-<br />
|Nov 8 || Neel Bhatt || 1 || Annotating Object Instances with a Polygon-RNN || [https://www.cs.utoronto.ca/~fidler/papers/paper_polyrnn.pdf Paper] || <br />
|-<br />
|Nov 8 || Jacob Manuel || 2 || || || <br />
|-<br />
|Nov 8 || Charupriya Sharma|| 2 || || || <br />
|-<br />
|NOv 13 || Sagar Rajendran || 1|| Zero-Shot Visual Imitation || [https://openreview.net/pdf?id=BkisuzWRW Paper] || <br />
|-<br />
|Nov 13 || Jiazhen Chen || 2|| || || <br />
|-<br />
|Nov 13 || Neil Budnarain || 2|| PixelNN: Example-Based Image Synthesis || [https://openreview.net/pdf?id=Syhr6pxCW Paper] || <br />
|-<br />
|NOv 15 || Zheng Ma || 3|| Reinforcement Learning of Theorem Proving || [https://arxiv.org/abs/1805.07563 Paper] || <br />
|-<br />
|Nov 15 || Abdul Khader Naik || 4|| || ||<br />
|-<br />
|Nov 15 || Johra Muhammad Moosa || 2|| Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin || [https://papers.nips.cc/paper/7255-attend-and-predict-understanding-gene-regulation-by-selective-attention-on-chromatin.pdf Paper] || <br />
|-<br />
|NOv 20 || Zahra Rezapour Siahgourabi || 1|| || || <br />
|-<br />
|Nov 20 || Shubham Koundinya || 6|| || || <br />
|-<br />
|Nov 20 || Salman Khan || || Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples || [http://proceedings.mlr.press/v80/athalye18a.html paper] || <br />
|-<br />
|NOv 22 ||Soroush Ameli || 3|| Learning to Navigate in Cities Without a Map || [https://arxiv.org/abs/1804.00168 paper] || <br />
|-<br />
|Nov 22 ||Ivan Li || 23 || Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate || [https://arxiv.org/pdf/1806.05161v2.pdf Paper] ||<br />
|-<br />
|Nov 22 ||Sigeng Chen || 2 || || ||<br />
|-<br />
|Nov 27 || Aileen Li || 8|| Spatially Transformed Adversarial Examples ||[https://openreview.net/pdf?id=HyydRMZC- Paper] || <br />
|-<br />
|NOv 27 ||Xudong Peng || 9|| Multi-Scale Dense Networks for Resource Efficient Image Classification || [https://openreview.net/pdf?id=Hk2aImxAb Paper] || <br />
|-<br />
|Nov 27 ||Xinyue Zhang || 10|| An Inference-Based Policy Gradient Method for Learning Options || [http://proceedings.mlr.press/v80/smith18a/smith18a.pdf Paper] || <br />
|-<br />
|NOv 29 ||Junyi Zhang || 11|| || || <br />
|-<br />
|Nov 29 ||Travis Bender || 12|| Automatic Goal Generation for Reinforcement Learning Agents || [http://proceedings.mlr.press/v80/florensa18a/florensa18a.pdf Paper] ||<br />
|-<br />
|Nov 29 ||Patrick Li || 12|| Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices || [https://www.cse.ust.hk/~huangzf/ICML18.pdf Paper] ||<br />
|-<br />
|Makup || Ruijie Zhang || 1 || Searching for Efficient Multi-Scale Architectures for Dense Image Prediction || [https://arxiv.org/pdf/1809.04184.pdf Paper]||<br />
|-<br />
|Makup || Ahmed Afify || 2||Don't Decay the Learning Rate, Increase the Batch Size || [https://openreview.net/pdf?id=B1Yy1BxCZ Paper]<br />
|-<br />
|Makup || Gaurav Sahu || 3 || TBD || ||<br />
|-<br />
|Makup || Kashif Khan || 4 || TBD || ||<br />
|-<br />
|Makup || Shala Chen || || A NEURAL REPRESENTATION OF SKETCH DRAWINGS || ||<br />
|-<br />
|Makup || Ki Beom Lee || || || ||<br />
|-<br />
|Makup || Wesley Fisher || || Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling || [http://proceedings.mlr.press/v80/lee18b/lee18b.pdf Paper] ||</div>Aghabuss