http://wiki.math.uwaterloo.ca/statwiki/api.php?action=feedcontributions&user=Y492zhu&feedformat=atomstatwiki - User contributions [US]2022-05-24T19:53:03ZUser contributionsMediaWiki 1.28.3http://wiki.math.uwaterloo.ca/statwiki/index.php?title=Loss_Function_Search_for_Face_Recognition&diff=49349Loss Function Search for Face Recognition2020-12-06T08:18:50Z<p>Y492zhu: /* Critiques */</p>
<hr />
<div>== Presented by ==<br />
Jan Lau, Anas Mahdi, Will Thibault, Jiwon Yang<br />
<br />
== Introduction ==<br />
Face recognition is a technology that can label a face to a specific identity. The field of study involves two tasks: 1. Identifying and classifying a face to a certain identity and 2. Verifying if this face image and another face image map to the same identity. Loss functions play an important role in evaluating how well the prediction models the given data. In the application of face recognition, they are used for training convolutional neural networks (CNNs) with discriminative features. However, traditional softmax loss lacks the power of feature discrimination. To solve this problem, a center loss was developed to learn centers for each identity to enhance the intra-class compactness. Hence, the paper introduced a new loss function using a scale parameter to produce higher gradients to well-separated samples which can reduce the softmax probability. <br />
<br />
Margin-based (angular, additive, additive angular margins) soft-max loss functions are important in learning discriminative features in face recognition. There have been hand-crafted methods previously developed that require much efforts such as A-softmax, V-softmax, AM-Softmax, and Arc-softmax. Li et al. proposed an AutoML for loss function search method also known as AM-LFS from a hyper-parameter optimization perspective [2]. It automatically determines the search space by leveraging reinforcement learning to the search loss functions during the training process, though the drawback is the complex and unstable search space.<br />
<br />
'''Soft Max'''<br />
Softmax probability is the probability for each class. It contains a vector of values that add up to 1 while ranging between 0 and 1. Cross-entropy loss is the negative log of the probabilities. When softmax probability is combined with cross-entropy loss in the last fully connected layer of the CNN, it yields the softmax loss function:<br />
<br />
<center><math>L_1=-\log\frac{e^{w^T_yx}}{e^{w^T_yx} + \sum_{k≠y}^K{e^{w^T_yx}}}</math> [1] </center><br />
<br />
<br />
Specifically for face recognition, <math>L_1</math> is modified such that <math>w^T_yx</math> is normalized and <math>s</math> represents the magnitude of <math>w^T_yx</math>:<br />
<br />
<center><math>L_2=-\log\frac{e^{s \cos{(\theta_{{w_y},x})}}}{e^{s \cos{(\theta_{{w_y},x})}} + \sum_{k≠y}^K{e^{s \cos{(\theta_{{w_y},x})}}}}</math> [1] </center><br />
<br />
Where <math> \cos{(\theta_{{w_k},x})} = w^T_y </math> is cosine similarity and <math>\theta_{{w_k},x}</math> is angle between <math> w_k</math> and x. The learnt features with this soft max loss are prone to be separable (as desired).<br />
<br />
'''Margin-based Softmax'''<br />
<br />
This function is crucial in face recognition because it is used for enhancing feature discrimination. While there are different variations of the softmax loss function, they build upon the same structure as the equation above.<br />
<br />
The margin-based softmax function is:<br />
<br />
<center><math>L_3=-\log\frac{e^{s f{(m,\theta_{{w_y},x})}}}{e^{s f{(m,\theta_{{w_y},x})}} + \sum_{k≠y}^K{e^{s \cos{(\theta_{{w_y},x})}}}} </math> </center><br />
<br />
Here, <math>f{(m,\theta_{{w_y},x})} \leq \cos (\theta_{w_y,x})</math> is a carefully chosen margin function.<br />
<br />
Some other variations of chosen functions:<br />
<br />
'''A-Softmax Loss:''' <math>f{(m_1,\theta_{{w_y},x})} = \cos (m_1\theta_{w_y,x})</math> , where m1 >= 1 and a integer.<br />
<br />
'''Arc-Softmax Loss:'''<math>f{(m_1,\theta_{{w_y},x})} = \cos (\theta_{w_y,x} + m_2)</math>, where m2 > 0<br />
<br />
'''AM-Softmax Loss:'''<math>f{(m,\theta_{{w_y},x})} = \cos (m_1\theta_{w_y,x} + m_2) - m_3</math>, where m1 >= 1 and a integer; m2,m3 > 0<br />
<br />
<br />
<br />
In this paper, the authors first identified that reducing the softmax probability is a key contribution to feature discrimination and designed two design search spaces (random and reward-guided method). They then evaluated their Random-Softmax and Search-Softmax approaches by comparing the results against other face recognition algorithms using nine popular face recognition benchmarks.<br />
<br />
== Motivation ==<br />
Previous algorithms for facial recognition frequently rely on CNNs that may include metric learning loss functions such as contrastive loss or triplet loss. Without sensitive sample mining strategies, the computational cost for these functions is high. This drawback prompts the redesign of classical softmax loss that cannot discriminate features. Multiple softmax loss functions have since been developed, and including margin-based formulations, they often require fine-tuning of parameters and are susceptible to instability. Therefore, researchers need to put in a lot of effort in creating their method in the large design space. AM-LFS takes an optimization approach for selecting hyperparameters for the margin-based softmax functions, but its aforementioned drawbacks are caused by the lack of direction in designing the search space.<br />
<br />
To solve the issues associated with hand-tuned softmax loss functions and AM-LFS, the authors attempt to reduce the softmax probability to improve feature discrimination when using margin-based softmax loss functions. The development of margin-based softmax loss with only one required parameter and an improved search space using a reward-based method was determined by the authors to be the best option for their loss function.<br />
<br />
== Problem Formulation ==<br />
=== Analysis of Margin-based Softmax Loss ===<br />
Based on the softmax probability and the margin-based softmax probability, the following function can be developed [1]:<br />
<br />
<center><math>p_m=\frac{1}{ap+(1-a)}*p</math></center><br />
<center> where <math>a=1-e^{s{cos{(\theta_{w_y},x)}-f{(m,\theta_{w_y},x)}}}</math> and <math>a≤0</math></center><br />
<br />
<math>a</math> is considered as a modulating factor and <math>h{(a,p)}=\frac{1}{ap+(1-a)} \in (0,1]</math> is a modulating function [1]. Therefore, regardless of the margin function (<math>f</math>), the minimization of the softmax probability will ensure success.<br />
<br />
Compared to AM-LFS, this method involves only one parameter (<math>a</math>) that is also constrained, versus AM-LFS which has 2M parameters without constraints that specify the piecewise linear functions the method requires. Also, the piecewise linear functions of AM-LFS (<math>p_m={a_i}p+b_i</math>) may not be discriminative because it could be larger than the softmax probability.<br />
<br />
=== Random Search ===<br />
Unified formulation <math>L_5</math> is generated by inserting a simple modulating function <math>h{(a,p)}=\frac{1}{ap+(1-a)}</math> into the original softmax loss. It can be written as below [1]:<br />
<br />
<center><math>L_5=-log{(h{(a,p)}*p)}</math> where <math>h \in (0,1]</math> and <math>a≤0</math></center><br />
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This encourages the feature margin between different classes and has the capability of feature discrimination. This leads to defining the search space as the choice of <math>h{(a,p)}</math> whose impacts on the training procedure are decided by the modulating factor <math>a</math>. In order to validate the unified formulation, a modulating factor is randomly set at each training epoch. This is noted as Random-Softmax in this paper.<br />
<br />
=== Reward-Guided Search ===<br />
Random search has no guidance for training. To solve this, the authors use reinforcement learning. Unlike supervised learning, reinforcement learning (RL) is a behavioral learning model. It does not need to have input/output labelled and it does not need a sub-optimal action to be explicitly corrected. The algorithm receives feedback from the data to achieve the best outcome. The system has an agent that guides the process by taking an action that maximizes the notion of cumulative reward [3]. The process of RL is shown in figure 1. The equation of the cumulative reward function is: <br />
<br />
<center><math>G_t \overset{\Delta}{=} R_t+R_{t+1}+R_{t+2}+⋯+R_T</math></center><br />
<br />
where <math>G_t</math> = cumulative reward, <math>R_t</math> = immediate reward, and <math>R_T</math> = end of episode.<br />
<br />
<math>G_t</math> is the sum of immediate rewards from arbitrary time <math>t</math>. It is a random variable because it depends on the immediate reward which depends on the agent action and the environment's reaction to this action.<br />
<br />
<center>[[Image:G25_Figure1.png|300px |link=https://en.wikipedia.org/wiki/Reinforcement_learning#/media/File:Reinforcement_learning_diagram.svg |alt=Alt text|Title text]]</center><br />
<center>Figure 1: Reinforcement Learning scenario [4]</center><br />
<br />
The reward function is what guides the agent to move in a certain direction. As mentioned above, the system receives feedback from the data to achieve the best outcome. This is caused by the reward being edited based on the feedback it receives when a task is completed [5]. <br />
<br />
In this paper, RL is being used to generate a distribution of the hyperparameter <math>\mu</math> for the SoftMax equation using the reward function. At each epoch, <math>B</math> hyper-parameters <math>{a_1, a_2, ..., a_B }</math> are sampled as <math>a \sim \mathcal{N}(\mu, \sigma)</math>. In each epoch, <math>B</math> models are generated with rewards <math>R(a_i), i \in [1, B]</math>. <math>\mu</math> updates after each epoch from the reward function. <br />
<br />
<center><math>\mu_{e+1}=\mu_e + \eta \frac{1}{B} \sum_{i=1}^B R{(a_i)}{\nabla_a}log{(g(a_i;\mu,\sigma))}</math></center><br />
<br />
Where <math>{g(a_i; \mu, \sigma})</math> is the PDF of a Gaussian distribution. The distributions of <math>{a}</math> are updated and the best model if found from the <math>{B}</math> candidates for the next epoch.<br />
<br />
=== Optimization ===<br />
Calculating the reward involves a standard bi-level optimization problem. A standard bi-level optimization problem is a hierarchy of two optimization tasks, an upper-level or leader and lower-level or follower problems, which involves a hyperparameter ({<math>a_1,a_2,…,a_B</math>}) that can be used for minimizing one objective function while maximizing another objective function simultaneously:<br />
<br />
<center><math>max_a R(a)=r(M_{w^*(a)},S_v)</math></center><br />
<center><math>w^*(a)=_w \sum_{(x,y) \in S_t} L^a (M_w(x),y)</math></center><br />
<br />
In this case, the loss function takes the training set <math>S_t</math> and the reward function takes the validation set <math>S_v</math>. The weights <math>w</math> are trained such that the loss function is minimized while the reward function is maximized. The calculated reward for each model ({<math>M_{we1},M_{we2},…,M_{weB}</math>}) yields the corresponding score, then the algorithm chooses the one with the highest score for model index selection. With the model containing the highest score being used in the next epoch, this process is repeated until the training reaches convergence. In the end, the algorithm takes the model with the highest score without retraining.<br />
<br />
== Results and Discussion ==<br />
=== Data Preprocessing ===<br />
The training datasets consisted of cleaned versions of CASIA-WebFace and MS-Celeb-1M-v1c to remove the impact of noisy labels in the original sets.<br />
Furthermore, it is important to perform open-set evaluation for face recognition problem. That is, there shall be no overlapping identities between training and testing sets. As a result, there were a total of 15,414 identities removed from the testing sets. For fairness during comparison, all summarized results will be based on refined datasets.<br />
<br />
=== Results on LFW, SLLFW, CALFW, CPLFW, AgeDB, DFP ===<br />
For LFW, there is not a noticeable difference between the algorithms proposed in this paper and the other algorithms, however, AM-Softmax achieved higher results than Search-Softmax. Random-Softmax achieved the highest results by 0.03%.<br />
<br />
Random-Softmax outperforms baseline Soft-max and is comparable to most of the margin-based softmax. Search-Softmax boost the performance and better most methods specifically when training CASIA-WebFace-R data set, it achieves 0.72% average improvement over AM-Softmax. The reason the model proposed by the paper gives better results is because of their optimization strategy which helps boost the discimination power. Also the sampled candidate from the paper’s proposed search space can well approximate the margin-based loss functions. More tests need to happen to more complicated protocols to test the performance further. Not a lot of improvement has been shown on those test sets, since they are relatively simple and the performance of all the methods on these test sets are near saturation. The following table gives a summary of the performance of each model.<br />
<br />
<center>Table 1.Verification performance (%) of different methods on the test sets LFW, SLLFW, CALFW, CPLFW, AgeDB and CFP. The training set is '''CASIA-WebFace-R''' [1].</center><br />
<br />
<center>[[Image:G25_Table1.png|900px |alt=Alt text|Title text]]</center><br />
<br />
=== Results on RFW ===<br />
The RFW dataset measures racial bias which consists of Caucasian, Indian, Asian, and African. Using this as the test set, Random-softmax and Search-softmax performed better than the other methods. Random-softmax outperforms the baseline softmax by a large margin which means reducing the softmax probability will enhance the feature discrimination for face recognition. It is also observed that the reward guided search-softmax method is more likely to enhance the discriminative feature learning resulting in higher performance as shown in Table 2 and Table 3. <br />
<br />
<center>Table 2. Verification performance (%) of different methods on the test set RFW. The training set is '''CASIA-WebFace-R''' [1].</center><br />
<center>[[Image:G25_Table2.png|500px |alt=Alt text|Title text]]</center><br />
<br />
<br />
<center>Table 3. Verification performance (%) of different methods on the test set RFW. The training set is '''MS-Celeb-1M-v1c-R''' [1].</center><br />
<center>[[Image:G25_Table3.png|500px |alt=Alt text|Title text]]</center><br />
<br />
=== Results on MegaFace and Trillion-Pairs ===<br />
The different loss functions are tested again with more complicated protocols. The identification (Id.) Rank-1 and the verification (Veri.) with the true positive rate (TPR) at low false acceptance rate (FAR) at <math>1e^{-3}</math> on MegaFace, the identification TPR@FAR = <math>1e^{-6}</math> and the verification TPR@FAR = <math>1e^{-9}</math> on Trillion-Pairs are reported on Table 4 and 5.<br />
<br />
On the test sets MegaFace and Trillion-Pairs, Search-Softmax achieves the best performance over all other alternative methods. On MegaFace, Search-Softmax beat the best competitor AM-softmax by a large margin. It also outperformed AM-LFS due to new designed search space. <br />
<br />
<center>Table 4. Performance (%) of different loss functions on the test sets MegaFace and Trillion-Pairs. The training set is '''CASIA-WebFace-R''' [1].</center><br />
<center>[[Image:G25_Table4.png|450px |alt=Alt text|Title text]]</center><br />
<br />
<br />
<center>Table 5. Performance (%) of different loss functions on the test sets MegaFace and Trillion-Pairs. The training set is '''MS-Celeb-1M-v1c-R''' [1].</center><br />
<center>[[Image:G25_Table5.png|450px |alt=Alt text|Title text]]</center><br />
<br />
From the CMC curves and ROC curves in Figure 2, similar trends are observed at other measures. There is a similar trend with Trillion-Pairs where Search-Softmax loss is found to be superior with 4% improvements with CASIA-WebFace-R and 1% improvements with MS-Celeb-1M-v1c-R at both the identification and verification. Based on these experiments, Search-Softmax loss can perform well, especially with a low false positive rate and it shows a strong generalization ability for face recognition.<br />
<br />
<center>[[Image:G25_Figure2_left.png|800px |alt=Alt text|Title text]] [[Image:G25_Figure2_right.png|800px |alt=Alt text|Title text]]</center><br />
<center>Figure 2. From Left to Right: CMC curves and ROC curves on MegaFace Set with training set CASIA-WebFace-R, CMC curves and ROC curves on MegaFace Set with training set MS-Celeb-1M-v1c-R [1].</center><br />
<br />
== Conclusion ==<br />
The paper discussed that in order to enhance feature discrimination for face recognition, it is crucial to reduce the softmax probability. To achieve this goal, unified formulation for the margin-based softmax losses is designed. Two search methods have been developed using a random and a reward-guided loss function and they were validated to be effective over six other methods using nine different test data sets. While these developed methods were generally more effective in increasing accuracy versus previous methods, there is very little difference between the two. It can be seen that Search-Softmax performs slightly better than Random-Softmax most of the time.<br />
<br />
== Critiques ==<br />
* Thorough experimentation and comparison of results to state-of-the-art provided a convincing argument.<br />
* Datasets used did require some preprocessing, which may have improved the results beyond what the method otherwise would.<br />
* AM-LFS was created by the authors for experimentation (the code was not made public) so the comparison may not be accurate.<br />
* The test data set they used to test Search-Softmax and Random-Softmax are simple and they saturate in other methods. So the results of their methods didn’t show many advantages since they produce very similar results. A more complicated data set needs to be tested to prove the method's reliability.<br />
* There is another paper Large-Margin Softmax Loss for Convolutional Neural Networks[https://arxiv.org/pdf/1612.02295.pdf] that provides a more detailed explanation about how to reduce margin-based softmax loss.<br />
* It is questionable when it comes to the accuracy of testing sets, as they only used the clean version of CASIA-WebFace and MS-Celeb-1M-vlc for training instead of these two training sets with noisy labels.<br />
* In a similar [https://arxiv.org/pdf/1905.09773.pdf?utm_source=thenewstack&utm_medium=website&utm_campaign=platform paper], written by Tae-Hyun Oh et al., they also discuss an optimal loss function for face recognition. However, since in the other paper, they were doing face recognition from voice audio, the loss function used was slightly different than the ones discussed in this paper.<br />
* This model has many applications such as identifying disguised prisoners for police. But we need to do a good data preprocessing otherwise we might not get a good predicted result. But authors did not mention about the data preprocessing which is a key part of this model.<br />
* It will be better if we can know what kind of noises was removed in the clean version. Also, simply removing the overlapping data is wasteful. It would be better to just put them into one of the train and test samples.<br />
* This paper indicate that the new searching method and loss function have induced more effective face recognition result than other six methods. But there is no mention of the increase or decrease in computational efficiency since only very little difference exist between those methods and the real time evaluation is often required at the face recognition application level.<br />
* There are some loss functions that receives more than 2 inputs. For example, the ''triplet loss'' function, developed by Google, takes 3 inputs: positive input, negative input and anchor input. This makes sense because for face recognition, we want to model to learn not only what it is supposed to predict but also what it is not supposed to predict. Typically, triplet loss handles false positives much better. This paper can extend its scope to such loss function that takes more than 2 inputs.<br />
* It would be good to also know what the training time is like for the method, specifically the "Reward-Guided Search" which uses RL. Also the authors mention some data preprocessing that was performed, was this same preprocessing also performed for the methods they compared against?<br />
* Sections on Data Processing and Results can be improved. About the datasets, I have some questions about why they are divided in the current fashion. It is mentioned that "CASIA-WebFace and MS-Celeb-1M-v1c" are used as training datasets. But the comparison of algorithms are divided into three groups: Megaface and TrillionPairs, RFW, and a group of other datasets. In general, when we are comparing algorithms, we want to have a holistic view of how each algorithm compare. So I have some concerns about dividing the results into three section. More explanation can be provided. It also seems like Random-Softmax and Search Softmax outperform all other algorithms across all datasets. So it would make even more sense to have a big table including all the results. About data preprocessing, I believe that giving more information about which noisy data are removed would be nice.<br />
* Despite thorough comparison between each method against the proposed method, it does not give a reason to why it was the case that it was either better or worse, and it does not necessarily need to be a mathematical explanation but an intuitive one to demonstrate how it can be replicated and whether the results require a certain condition to achieve. <br />
* Though we have a graph demonstrating the training loss with Random-Softmax and Search-Softmax with regards to the number of Epochs as an independent variable which we may deduce the number of epochs used in later graphs but since one of the main features is that "Meanwhile, our optimization strategy enables that the dynamic loss can guide<br />
* Did the paper address why the average model performs worse on African faces, would it be a lack of data points?<br />
the model training of different epochs, which helps further boost the discrimination power." it is imperative that the results are comparable along the same scale (for example, for 20 epochs, then take the average of the losses).<br />
* The result summary is overwhelming with numbers and representation of result is lacking. It would be great if the result can be explained. Introduction of model and its component is lacking and could be explained more.<br />
* The introduction of data and the analysis of data processing are important because there might be some limitations. Also, it would be better to give theoretical analysis of the effects of reducing softmax probability and the number of sampled models, which explains the update of the parameters for better performance.<br />
* It would be better to include time performance in the evaluation section.<br />
<br />
== References ==<br />
[1] X. Wang, S. Wang, C. Chi, S. Zhang and T. Mei, "Loss Function Search for Face Recognition", in International Conference on Machine Learning, 2020, pp. 1-10.<br />
<br />
[2] Li, C., Yuan, X., Lin, C., Guo, M., Wu, W., Yan, J., and Ouyang, W. Am-lfs: Automl for loss function search. In Proceedings of the IEEE International Conference on Computer Vision, pp. 8410–8419, 2019.<br />
2020].<br />
<br />
[3] S. L. AI, “Reinforcement Learning algorithms - an intuitive overview,” Medium, 18-Feb-2019. [Online]. Available: https://medium.com/@SmartLabAI/reinforcement-learning-algorithms-an-intuitive-overview-904e2dff5bbc. [Accessed: 25-Nov-2020]. <br />
<br />
[4] “Reinforcement learning,” Wikipedia, 17-Nov-2020. [Online]. Available: https://en.wikipedia.org/wiki/Reinforcement_learning. [Accessed: 24-Nov-2020].<br />
<br />
[5] B. Osiński, “What is reinforcement learning? The complete guide,” deepsense.ai, 23-Jul-2020. [Online]. Available: https://deepsense.ai/what-is-reinforcement-learning-the-complete-guide/. [Accessed: 25-Nov-2020].</div>Y492zhuhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Speech2Face:_Learning_the_Face_Behind_a_Voice&diff=49345Speech2Face: Learning the Face Behind a Voice2020-12-06T08:03:48Z<p>Y492zhu: /* Discussion and Critiques */</p>
<hr />
<div>== Presented by == <br />
Ian Cheung, Russell Parco, Scholar Sun, Jacky Yao, Daniel Zhang<br />
<br />
== Introduction ==<br />
This paper presents a deep neural network architecture called Speech2Face. This architecture utilizes millions of Internet/Youtube videos of people speaking to learn the correlation between a voice and the respective face. The model learns the correlations, allowing it to produce facial reconstruction images that capture specific physical attributes, such as a person's age, gender, or ethnicity, through a self-supervised procedure. Namely, the model utilizes the simultaneous occurrence of faces and speech in videos and does not need to model the attributes explicitly. This model explores what types of facial information could be extracted from speech without the constraints of predefined facial characterizations. Without any prior information or accurate classifiers, the reconstructions revealed correlations between craniofacial features and voice in addition to the correlation between dominant features (gender, age, ethnicity, etc.) and voice. The model is evaluated and numerically quantifies how closely the reconstruction, done by the Speech2Face model, resembles the true face images of the respective speakers.<br />
<br />
== Ethical Considerations ==<br />
<br />
The authors note that due to the potential sensitivity of facial information, they have chosen to explicitly state some ethical considerations. The first of which is privacy. The paper states that the method cannot recover the true identity of the face or produce faces of specific individuals, but rather will show average-looking faces. The paper also addresses that there are potential dataset biases that exist for the voice-face correlations, thus the faces may not accurately represent the intended population. Finally, it acknowledges that the model uses demographic categories that are defined by a commercial face attribute classifier.<br />
<br />
== Previous Work ==<br />
With visual and audio signals being so dominant and accessible in our daily life, there has been huge interest in how visual and audio perceptions interact with each other. Arandjelovic and Zisserman [1] leveraged the existing database of mp4 files to learn a generic audio representation to classify whether a video frame and an audio clip correspond to each other. These learned audio-visual representations have been used in a variety of setting, including cross-modal retrieval, sound source localization and sound source separation. This also paved the path for specifically studying the association between faces and voices of agents in the field of computer vision. In particular, cross-modal signals extracted from faces and voices have been proposed as a binary or multi-task classification task and there have been some promising results. Studies have been able to identify active speakers of a video, separate speech from multiple concurrent sources, predict lip motion from speech, and even learn the emotion of the agents based on their voices. Aytar et al. [6] proposed a student-teacher training procedure in which a well established visual recognition model was used to transfer the knowledge obtained in the visual modality to the sound modality, using unlabeled videos.<br />
<br />
Recently, various methods have been suggested to use various audio signals to reconstruct visual information, where the reconstructed subject is subjected to a priori. Notably, Duarte et al. [2] were able to synthesize the exact face images and expression of an agent from speech using a GAN model. A generative adversarial network (GAN) model is one that uses a generator to produce seemingly possible data for training and a discriminator that identifies if the training data is fabricated by the generator or if it is real [7]. This paper instead hopes to recover the dominant and generic facial structure from a speech.<br />
<br />
== Motivation ==<br />
It seems to be a common trait among humans to imagine what some people look like when we hear their voices before we have seen what they look lke. There is a strong connection between speech and appearance, which is a direct result of the factors that affect speech, including age, gender, and facial bone structure. In addition, other voice-appearance correlations stem from the way in which we talk: language, accent, speed, pronunciations, etc. These properties of speech are often common among many different nationalities and cultures, which can, in turn, translate to common physical features among different voices. Namely, from an input audio segment of a person speaking, the method would reconstruct an image of the person’s face in a canonical form (frontal-facing, neutral expression). The goal was to study to what extent people can infer how someone else looks from the way they talk. Rather than predicting a recognizable image of the exact face, the authors are more interested in capturing the dominant facial features.<br />
<br />
== Model Architecture == <br />
<br />
'''Speech2Face model and training pipeline'''<br />
<br />
[[File:ModelFramework.jpg|center]]<br />
<br />
<div style="text-align:center;"> Figure 1. '''Speech2Face model and training pipeline''' </div><br />
<br />
<br />
<br />
The Speech2Face Model used to achieve the desired result consists of two parts - a voice encoder which takes in a spectrogram of speech as input and outputs low dimensional face features, and a face decoder which takes in face features as input and outputs a normalized image of a face (neutral expression, looking forward). Figure 1 gives a visual representation of the pipeline of the entire model, from video input to a recognizable face. The combination of the voice encoder and face decoder results are combined to form an image. The variability in facial expressions, head positions and lighting conditions of the face images creates a challenge to both the design and training of the Speech2Face model. It needs a model to figure out many irrelevant variations in the data, and to implicitly extract important internal representations of faces. To avoid this problem the model is trained to first regress to a low dimensional intermediate representation of the face. <br />
<br />
'''Face Decoder''' <br />
The face decoder itself was taken from previous work The VGG-Face model by Cole et al [3] (a face recognition model that is pretrained on a largescale face database [5] is used to extract a 4069-D face feature from the penultimate layer of the network.) and will not be explored in great detail here, but in essence the facenet model is combined with a single multilayer perceptron layer, the result of which is passed through a convolutional neural network to determine the texture of the image, and a multilayer perception to determine the landmark locations. The face decoder kept the VGG-Face model's dimension and weights. The weights were also trained separately and remained fixed during the voice encoder training. <br />
<br />
'''Voice Encoder Architecture''' <br />
<br />
[[File:VoiceEncoderArch.JPG|center]]<br />
<br />
<div style="text-align:center;"> Table 1: '''Voice encoder architecture''' </div><br />
<br />
<br />
<br />
The voice encoder itself is a convolutional neural network, which transforms the input spectrogram into pseudo face features. The exact architecture is given in Table 1. The model alternates between convolution, ReLU, batch normalization layers, and layers of max-pooling. In each max-pooling layer, pooling is only done along the temporal dimension of the data. This is to ensure that the frequency, an important factor in determining vocal characteristics such as tone, is preserved. In the final pooling layer, an average pooling is applied along the temporal dimension. This allows the model to aggregate information over time and allows the model to be used for input speeches of varying lengths. Two fully connected layers at the end are used to return a 4096-dimensional facial feature output.<br />
<br />
'''Training'''<br />
<br />
The AVSpeech dataset, a large-scale audio-visual dataset is used for the training. AVSpeech dataset is comprised of millions of video segments from Youtube with over 100,000 different people. The training data is composed of educational videos and does not provide an accurate representation of the global population, which will clearly affect the model. Also note that facial features that are irrelevant to speech, like hair color, may be predicted by the model. From each video, a 224x224 pixels image of the face was passed through the face decoder to compute a facial feature vector. Combined with a spectrogram of the audio, a training and test set of 1.7 and 0.15 million entries respectively were constructed.<br />
<br />
The voice encoder is trained in a self-supervised manner. A frame that contains the face is extracted from each video and then inputted to the VGG-Face model to extract the feature vector <math>v_f</math>, the 4096-dimensional facial feature vector given by the face decoder on a single frame from the input video. This provides the supervision signal for the voice-encoder. The feature <math>v_s</math>, the 4096 dimensional facial feature vector from the voice encoder, is trained to predict <math>v_f</math>.<br />
<br />
In order to train this model, a proper loss function must be defined. The L1 norm of the difference between <math>v_s</math> and <math>v_f</math>, given by <math>||v_f - v_s||_1</math>, may seem like a suitable loss function, but in actuality results in unstable results and long training times. Figure 2, below, shows the difference in predicted facial features given by <math>||v_f - v_s||_1</math> and the following loss. Based on the work of Castrejon et al. [4], a loss function is used which penalizes the differences in the last layer of the VGG-Face model <math>f_{VGG}</math>: <math> \mathbb{R}^{4096} \to \mathbb{R}^{2622}</math> and the first layer of face decoder <math>f_{dec}</math> : <math> \mathbb{R}^{4096} \to \mathbb{R}^{1000}</math>. The final loss function is given by: $$L_{total} = ||f_{dec}(v_f) - f_{dec}(v_s)|| + \lambda_1||\frac{v_f}{||v_f||} - \frac{v_s}{||v_s||}||^2_2 + \lambda_2 L_{distill}(f_{VGG}(v_f), f_{VGG}(v_s))$$<br />
This loss penalizes on both the normalized Euclidean distance between the 2 facial feature vectors and the knowledge distillation loss, which is given by: $$L_{distill}(a,b) = -\sum_ip_{(i)}(a)\text{log}p_{(i)}(b)$$ $$p_{(i)}(a) = \frac{\text{exp}(a_i/T)}{\sum_j \text{exp}(a_j/T)}$$ Knowledge distillation is used as an alternative to Cross-Entropy. By recommendation of Cole et al [3], <math> T = 2 </math> was used to ensure a smooth activation. <math>\lambda_1 = 0.025</math> and <math>\lambda_2 = 200</math> were chosen so that magnitude of the gradient of each term with respect to <math>v_s</math> are of similar scale at the <math>1000^{th}</math> iteration.<br />
<br />
<center><br />
[[File:L1vsTotalLoss.png | 700px]]<br />
</center><br />
<br />
<div style="text-align:center;"> Figure 2: '''Qualitative results on the AVSpeech test set''' </div><br />
<br />
== Results ==<br />
<br />
'''Confusion Matrix and Dataset statistics'''<br />
<br />
<center><br />
[[File:Confusionmatrix.png| 600px]]<br />
</center><br />
<br />
<div style="text-align:center;"> Figure 3. '''Facial attribute evaluation''' </div><br />
<br />
<br />
<br />
In order to determine the similarity between the generated images and the ground truth, a commercial service known as Face++ which classifies faces for distinct attributes (such as gender, ethnicity, etc) was used. Figure 3 gives a confusion matrix based on gender, ethnicity, and age. By examining these matrices, it is seen that the Speech2Face model performs very well on gender, only misclassifying 6% of the time. Similarly, the model performs fairly well on ethnicities, especially with white or Asian faces. Although the model performs worse on black and Indian faces, that can be attributed to the vastly unbalanced data, where 50% of the data represented a white face, and 80% represented a white or Asian face. <br />
<br />
'''Feature Similarity'''<br />
<br />
<center><br />
[[File:FeatSim.JPG]]<br />
</center><br />
<br />
<div style="text-align:center;"> Table 2. '''Feature similarity''' </div><br />
<br />
<br />
<br />
Another examination of the result is the similarity of features predicted by the Speech2Face model. The cosine, L1, and L2 distance between the facial feature vector produced by the model and the true facial feature vector from the face decoder were computed, and presented, above, in Table 2. A comparison of facial similarity was also done based on the length of audio input. From the table, it is evident that the 6-second audio produced a lower cosine, L1, and L2 distance, resulting in a facial feature vector that is closer to the ground truth. <br />
<br />
'''S2F -> Face retrieval performance'''<br />
<br />
<center><br />
[[File: Retrieval.JPG]]<br />
</center><br />
<br />
<div style="text-align:center;"> Table 3. '''S2F -> Face retrieval performance''' </div><br />
<br />
<br />
<br />
The performance of the model was also examined on how well it could produce the original image. The R@K metric, also known as retrieval performance by recall at K, measures the probability that the K closest images to the model output includes the correct image of the speaker's face. A higher R@K score indicates better performance. From Table 3, above, we see that both the 3-second and 6-second audio showed significant improvement over random chance, with the 6-second audio performing slightly better.<br />
<br />
'''Additional Observations''' <br />
<br />
Ablation studies were carried out to test the effect of audio duration and batch normalization. It was found that the duration of input audio during the training stage had little effect on convergence speed (comparing 3 and 6-second speech segments), while in the test stage longer input speech yields improvement in reconstruction quality. With respect to batch normalization (BN), it was found that without BN reconstructed faces would converge to an average face, while the inclusion of BN led to results which contained much richer facial features.<br />
<br />
== Conclusion ==<br />
The report presented a novel study of face reconstruction from audio recordings of a person speaking. The model was demonstrated to be able to predict plausible face reconstructions with similar facial features to real images of the person speaking. The problem was addressed by learning to align the feature space of speech to that of a pretrained face decoder. The model was trained on millions of videos of people speaking from YouTube. The model was then evaluated by comparing the reconstructed faces with a commercial facial detection service. The authors believe that facial reconstruction allows a more comprehensive view of voice-face correlation compared to predicting individual features, which may lead to new research opportunities and applications.<br />
<br />
== Discussion and Critiques ==<br />
<br />
There is evidence that the results of the model may be heavily influenced by external factors:<br />
<br />
1. Their method of sampling random YouTube videos resulted in an unbalanced sample in terms of ethnicity. Over half of the samples were white. We also saw a large bias in the model's prediction of ethnicity towards white. The bias in the results shows that the model may be overfitting the training data and puts into question what the performance of the model would be when trained and tested on a balanced dataset. Figure (11) highlights this shortcoming: The same man heard speaking in either English or Chinese was predicted to have a "white" appearance or an "asian" appearance respectively.<br />
<br />
2. The model was shown to infer different face features based on language. This puts into question how heavily the model depends on the spoken language. The paper mentioned the quality of face reconstruction may be affected by uncommon languages, where English is the most popular language on Youtube(training set). Testing a more controlled sample where all speech recording was of the same language may help address this concern to determine the model's reliance on spoken language.<br />
<br />
3. The evaluation of the result is also highly dependent on the Face++ classifiers. Since they compare the age, gender, and ethnicity by running the Face++ classifiers on the original images and the reconstructions to evaluate their model, the model that they create can only be as good as the one they are using to evaluate it. Therefore, any limitations of the Face++ classifier may become a limitation of Speech2Face and may result in a compounding effect on the miss-classification rate.<br />
<br />
4. Figure 4.b shows the AVSpeech dataset statistics. However, it doesn't show the statistics about speakers' ethnicity and the language of the video. If we train the model with a more comprehensive dataset that includes enough Asian/Indian English speakers and native language speakers will this increase the accuracy?<br />
<br />
5. One concern about the source of the training data, i.e. the Youtube videos, is that resolution varies a lot since the videos are randomly selected. That may be the reason why the proposed model performs badly on some certain features. For example, it is hard to tell the age when the resolution is bad because the wrinkles on the face are neglected.<br />
<br />
6. The topic of this project is very interesting, but I highly doubt this model will be practical in real-world problems. Because there are many factors to affect a person's sound in a real-world environment. Sounds such as phone clock, TV, car horn and so on. These sounds will decrease the accuracy of the predicted result of the model.<br />
<br />
7. A lot of information can be obtained from someone's voice, this can potentially be useful for detective work and crime scene investigation. In our world of increasing surveillance, public voice recording is quite common and we can reconstruct images of potential suspects based on their voice. In order for this to be achieved, the model has to be thoroughly trained and tested to avoid false positives as it could have a highly destructive outcome for a falsely convicted suspect.<br />
<br />
8. This is a very interesting topic, and this summary has a good structure for readers. Since this model uses Youtube to train model, but I think one problem is that most of the YouTubers are adult, and many additional reasons make this dataset highly unbalanced. What is more, some people may have a baby voice, this also could affect the performance of the model. But overall, this is a meaningful topic, it might help police to locate the suspects. So it might be interesting to apply this to the police.<br />
<br />
9. In addition, it seems very unlikely that any results coming from this model would ever be held in regard even remotely close to being admissible in court to identify a person of interest until the results are improved and the model can be shown to work in real-world applications. Otherwise, there seems to be very little use for such technology and it could have negative impacts on people if they were to be depicted in an unflattering way by the model based on their voice.<br />
<br />
10. Using voice as a factor of constructing the face is a good idea, but it seems like the data they have will have lots of noise and bias. The voice of a video might not come from the person in the video. There are so many YouTubers adjusting their voices before uploading their video and it's really hard to know whether they adjust their voice. Also, most YouTubers are adults so the model cannot have enough training samples about teenagers and kids.<br />
<br />
11. It would be interesting to see how the performance changes with different face encoding sizes (instead of just 4096-D) and also difference face models (encoder/decoders) to see if better performance can be achieved. Also given that the dataset used was unbalanced, was the dataset used to train the face model the same dataset? or was a different dataset used (the model was pretrained). This could affect the performance of the model as well.<br />
<br />
12. The audio input is transformed into a spectrogram before being used for training. They use STFT with a Hann window of 25 mm, a hop length of 10 ms, and 512 FFT frequency bands. They cite this method from a paper that focuses on speech separation, not speech classification. So, it would be interesting to see if there is a better way to do STFT, possibly with different hyperparameters (eg. different windowing, different number of bands), or if another type of transform (eg. wavelet transform) would have better results.<br />
<br />
13. A easy way to get somewhat balanced data is to duplicate the data that are fewer.<br />
<br />
14. This problem is interesting but is hard to generalize. This algorithm didn't account for other genders and mixed-race. In addition, the face recognition software Face++ introduces bias which can carry forward to Speech2Face algorithm. Face recognition algorithms are known to have higher error rates classifying darker-skinned individuals. Thus, it'll be tough to apply it to real-life scenarios like identifying suspects.<br />
<br />
15. This experiment raises a lot of ethical complications when it comes to possible applications in the real world. Even if this model was highly accurate, the implications of being able to discern a person's racial ethnicity, skin tone, etc. based solely on there voice could play in to inherent biases in the application user and this may end up being an issue that needs to be combatted in future research in this area. Another possible issue is that many people will change their intonation or vocal features based on the context (I'll likely have a different voice pattern in a job interview in terms of projection, intonation, etc. than if I was casually chatting/mumbling with a friend while playing video games for example).<br />
<br />
16. Overall a very interesting topic. I want to talk about the technical challenged raised by using the AVSSpeech dataset for training. The paper acknowledges that the AVSSpeech is unbalanced, and 80% of the data are white and Asians. It also says in the results section that "Our model does not perform on other races due to the imbalance in data". There does not seem to be any effort made in balancing the data. I think that there are definitely some data processing techniques that can be used (filtering, data augmentation, etc) to address the class imbalance problem. Not seeing any of these in the paper is a bit disappointing. Another issue I have noticed is that the model aims to predict an average-looking face from certain gender/racial group from voice input, due to ethical considerations. If we cannot reveal the identify of a person, why don't we predict the gender and race directly? Giving an average-looking face does not seem to be the most helpful.<br />
<br />
17. Very interesting research paper to be studied and the main objective was also interesting. This research leads to open question which can be applied to another application such as predicting person's face using voice and can be used in more advanced way. The only risk is how the data is obtained from YouTube where data is not consistent.<br />
<br />
18. The essay uses millions of natural videos of people speaking to find the correlation between face and voice. Since face and voice are commonly used as the identity of a person, there are many possible research opportunities and applications about improving voice and face unlock.<br />
<br />
19. It would be better to have a future work section to discuss the current shortage and explore the possible improvement and applications in the future.<br />
<br />
20. While the idea behind Speech2Face is interesting, ethnic profiling is a huge concern and it can further lead to racial discrimination, racism etc. Developers must put more care and thought into applying Speech2Face in tech before deploying the products.<br />
<br />
21. It would be helpful if the author could explore the different applications of this project in real life. Speech2face can be helpful during criminal investigation and essentially in scenarios when someone's picture is missing and only voice is available. It would also be helpful if the author could state the importance and need of such kind project in the society.<br />
<br />
22. The authors mention that they use the AVSpeech dataset for both training and testing but do not talk about how they split the data. It is possible that the same speakers were used in the training and testing data and so the model is able to recreate a face simply by matching the observed face to the observed audio. This would explain the striking example images shown in the paper.<br />
<br />
23. Another interesting application of this research is automated speech or facial animation at scale or in multiple languages. The cutting-edge automated facial animation solution provided by JALI Research Inc is applied in Cyberpunk 2077.<br />
<br />
== References ==<br />
[1] R. Arandjelovic and A. Zisserman. Look, listen and learn. In<br />
IEEE International Conference on Computer Vision (ICCV),<br />
2017.<br />
<br />
[2] A. Duarte, F. Roldan, M. Tubau, J. Escur, S. Pascual, A. Salvador, E. Mohedano, K. McGuinness, J. Torres, and X. Giroi-Nieto. Wav2Pix: speech-conditioned face generation using generative adversarial networks. In IEEE International<br />
Conference on Acoustics, Speech and Signal Processing<br />
(ICASSP), 2019.<br />
<br />
[3] F. Cole, D. Belanger, D. Krishnan, A. Sarna, I. Mosseri, and W. T. Freeman. Synthesizing normalized faces from facial identity features. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.<br />
<br />
[4] L. Castrejon, Y. Aytar, C. Vondrick, H. Pirsiavash, and A. Torralba. Learning aligned cross-modal representations from weakly aligned data. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.<br />
<br />
[5] O. M. Parkhi, A. Vedaldi, and A. Zisserman. Deep face recognition. In British Machine Vision Conference (BMVC), 2015.<br />
<br />
[7] “Overview of GAN Structure | Generative Adversarial Networks,” ''Google Developers'', 24-May-2019. [Online]. Available: https://developers.google.com/machine-learning/gan/gan_structure. [Accessed: 02-Dec-2020].</div>Y492zhuhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Evaluating_Machine_Accuracy_on_ImageNet&diff=49171Evaluating Machine Accuracy on ImageNet2020-12-05T02:54:08Z<p>Y492zhu: /* Critiques */</p>
<hr />
<div>== Presented by == <br />
Siyuan Xia, Jiaxiang Liu, Jiabao Dong, Yipeng Du<br />
<br />
== Introduction == <br />
ImageNet is the most influential data set in machine learning with images and corresponding labels over 1000 classes. This paper intends to explore the causes for performance differences between human experts and machine learning models, more specifically, CNN, on ImageNet. <br />
<br />
Firstly, some images could belong to multiple classes. As a result, it is possible to underestimate the performance if we assign each image with only one label, which is what is being done in the top-1 metric. Therefore, we adopt both top-1 and top-5 metrics where the performances of models, unlike human labelers, are linearly correlated in both cases.<br />
<br />
Secondly, in contrast to the uniform performance of models in classes, humans tend to achieve better performances on inanimate objects. Human labelers achieve similar overall accuracies as the models, which indicates spaces of improvements on specific classes for machines.<br />
<br />
Lastly, the setup of drawing training and test sets from the same distribution may favor models over human labelers. That is, the accuracy of multi-class prediction from models drops when the testing set is drawn from a different distribution than the training set, ImageNetV2. But this shift in distribution does not cause a problem for human labelers.<br />
<br />
== Experiment Setup ==<br />
=== Overview ===<br />
There are four main phases to the experiment, which are (i) initial multilabel annotation, (ii) human labeler training, (iii) human labeler evaluation, and (iv) final annotation overview. The five authors of the paper are the participants in the experiments. <br />
<br />
A brief overview of the four phases is as follows:<br />
[[File:Experiment Set Up.png |800px| center]]<br />
<br />
=== Initial multi-label annotation ===<br />
Three labelers A, B, and C provided multi-label annotations for a subset from the ImageNet validation set, and all images from the ImageNetV2 test sets. These experiences give A, B, and C extensive experience with the ImageNet dataset. <br />
<br />
=== Human Labeler Training === <br />
All five labelers trained on labeling a subset of the remaining ImageNet images. "Training" the human labelers consisted of teaching the humans the distinctions between very similar classes in the training set. For example, there are 118 classes of "dog" within ImageNet and typical human participants will not have working knowledge of the names of each breed of dog seen even if they can recognize and distinguish that breed from others.<br />
<br />
=== Human Labeler Evaluation ===<br />
Class-balanced random samples, which contains 1,000 images from the 20,000 annotated images are generated from both the ImageNet validation set and ImageNetV2. Five participants labeled these images over 28 days.<br />
<br />
=== Final annotation Review ===<br />
All labelers reviewed the additional annotations generated in the human labeler evaluation phase.<br />
<br />
== Multi-label annotations==<br />
[[File:Categories Multilabel.png|800px|center]]<br />
<div align="center">Figure 3</div><br />
<br />
===Top-1 accuracy===<br />
With Top-1 accuracy being the standard accuracy measure used in classification studies, it measures the proportions of examples for which the predicted label matches the single target label. As many images often contain more than one object for classification, for example, Figure 3a contains a desk, laptop, keyboard, space bar, and more. With Figure 3b showing a centered prominent figure yet labeled otherwise (people vs picket fence), it can be seen how a single target label is inaccurate for such a task since identifying the main objects in the image does not suffice due to its overly stringent and punishes predictions that are the main image yet does not match its label.<br />
===Top-5 accuracy===<br />
With Top-5 considers a classification correct if the object label is in the top 5 predicted labels, it partially resolves the problem with Top-1 labeling yet it is still not ideal since it can trivialize class distinctions. For instance, within the dataset, five turtle classes are given which is difficult to distinguish under such classification evaluations.<br />
===Multi-label accuracy===<br />
The paper then proposes that for every image, the image shall have a set of target labels and a prediction; if such prediction matches one of the labels, it will be considered as correct labeling. Due to the above-discussed limitations of Top-1 and Top-5 metrics, the paper claims it is necessary for rigorous accuracy evaluation on the dataset. <br />
<br />
===Types of Multi-label annotations===<br />
====Multiple objects or organisms====<br />
For the images containing more than one object or organism that corresponds to ImageNet, the paper proposed to add an additional target label for each entity in the image. With the discussed image in Figure 3b, the class groom, bow tie, suit, gown, and hoopskirt are all present in the foreground which is then subsequently added to the set of labels.<br />
====Synonym or subset relations====<br />
For similar classes, the paper considers them as under the same bigger class, that is, for two similarly labeled images, classification is considered correct if the produced label matches either one of the labels. For instance, warthog, African elephant, and Indian element all have prominent tusks, they will be considered subclasses of the tusker, Figure 3c shows a modification of labels to contain tusker as a correct label.<br />
====Unclear Image====<br />
In certain cases such as Figure 3d, there is a distinctive difficulty to determine whether a label was correct due to ambiguities in the class hierarchy.<br />
===Collecting multi-label annotations===<br />
Participants reviewed all predictions made by the models on the dataset ImageNet and ImageNet-V2, the participants then categorized every unique prediction made by the models on the dataset into correct and incorrect labels in order to allow all images to have multiple correct labels to satisfy the above-listed method.<br />
===The multi-label accuracy metric===<br />
One prediction is only correct if and only if it was marked correct by the expert reviewers during the annotation stage. As discussed in the experiment setup section, after human labelers have completed labeling, a second annotation stage is conducted. In Figure 4, a comparison of Top-1, Top-5, and multi-label accuracies showed higher Top-1 and Top-5 accuracy corresponds with higher multi-label accuracy as expected. With multi-label accuracies measures consistently higher than Top-1 yet lower than Top-5 which shows a high correlation between the three metrics, the paper concludes that multi-label metrics measures a semantically more meaningful notion of accuracy compared to its counterparts.<br />
<br />
== Human Accuracy Measurement Process ==<br />
=== Bias Control ===<br />
Since three participants participated in the initial round of annotation, they did not look at the data for six months, and two additional annotators are introduced in the final evaluation phase to ensure fairness of the experiment. <br />
<br />
=== Human Labeler Training ===<br />
The three main difficulties encountered during human labeler training are fine-grained distinctions, class unawareness, and insufficient training images. Thus, three training regimens are provided to address the problems listed above, respectively. First, labelers will be assigned extra training tasks with immediate feedbacks on similar classes. Second, labelers will be provided access to search for specific classes during labeling. Finally, the training set will contain a reasonable amount of images for each class.<br />
<br />
=== Labeling Guide ===<br />
A labeling guide is constructed to distill class analysis learned during training into discriminative traits that could be used as a reference during the final labeling evaluation.<br />
<br />
=== Final Evaluation and Review ===<br />
Two samples, each containing 1000 images, are sampled from ImageNet and ImageNetV2, respectively, They are sampled in a class-balanced manner and shuffled together. Over 28 days, all five participants labeled all images. They spent a median of 26 seconds per image. After labeling is completed, an additional multi-label annotation session was conducted, in which human predictions for all images are manually reviewed. Comparing to the initial round of labeling, 37% of the labels changes due to participants' greater familiarity with the classes.<br />
<br />
== Main Results ==<br />
[[File:Evaluating Machine Accuracy on ImageNet Figure 1.png | center]]<br />
<br />
<div align="center">Figure 1</div><br />
<br />
===Comparison of Human and Machine Accuracies on Image Net===<br />
From Figure 1, we can see that the difference in accuracies between the datasets is within 1% for all human participants. As hypothesized, human testers indeed performed better than the automated models on both datasets. It's worth noticing that labelers D and E, who did not participate in the initial annotation period, actually performed better than the best automated model.<br />
===Comparison of Human and Machine Accuracies on Image Net===<br />
Based on the results shown in Figure 1, we can see that the confidence interval of the best 4 human participants and 4 best model overlap; however, with a p-value of 0.037 using the McNemar's paired test, it rejects the hypothesis that the FixResNeXt model and Human E labeler have the same accuracy with respect to the ImageNet validation dataset. Figure 1 also shows that the confidence intervals of the labeling accuracies for human labelers C, D, E do not overlap with the confidence interval of the best model with respect to ImageNet-V2 and with the McNemar's test yielding a p-value of <math>2\times 10^{-4}</math>, it is clear that the hypothesis human and machined models have same robustness to model distribution shifts ought to be rejected.<br />
<br />
== Other Observations ==<br />
<br />
[[File: Results_Summary_Table.png| 800px|center]]<br />
<br />
=== Difficult Images ===<br />
<br />
The experiment also shed some light on images that are difficult to label. 10 images were misclassified by all of the human labelers. Among those 10 images, there was 1 image of a monkey and 9 of dogs. In addition, 27 images, with 19 in object classes and 8 in organism classes, were misclassified by all 72 machine learning models in this experiment. Only 2 images were labeled wrong by all human labelers and models. Both images contained dogs. Researchers also noted that difficult images for models are mostly images of objects and exclusively images of animals for human labelers.<br />
<br />
=== Accuracies without dogs ===<br />
<br />
As previously discussed in the paper, machine learning models tend to outperform human labelers when classifying the 118 dog classes. To better understand to what extent does models outperform human labelers, researchers computed the accuracies again by excluding all the dog classes. Results showed a 0.6% increase in accuracy on the ImageNet images using the best model and a 1.1% increase on the ImageNet V2 images. In comparison, the mean increases in accuracy for human labelers are 1.9% and 1.8% on the ImageNet and ImageNet V2 images respectively. Researchers also conducted a simulation to demonstrate that the increase in human labeling accuracy on non-dog images is significant. This simulation was done by bootstrapping to estimate the changes in accuracy when only using data for the non-dog classes, and simulation results show smaller increases than in the experiment. <br />
<br />
In conclusion, it's more difficult for human labelers to classify images with dogs than it is for machine learning models.<br />
<br />
=== Accuracies on objects ===<br />
Researchers also computed machine and human labelers' accuracies on a subset of data with only objects, as opposed to organisms, to better illustrate the differences in performance. This test involved 590 object classes. As shown in the table above, there is a 3.3% and 3.4% increase in mean accuracies for human labelers on the ImageNet and ImageNet V2 images. In contrast, there is a 0.5% decrease in accuracy for the best model on both ImageNet and ImageNet V2. This indicates that human labelers are much better at classifying objects than these models are.<br />
<br />
=== Accuracies on fast images ===<br />
Unlike the CNN models, human labelers spent different amounts of time on different images, spanning from several seconds to 40 minutes. To further analyze the images that take human labelers less time to classify, researchers took a subset of images with median labeling time spent by human labelers of at most 60 seconds. These images were referred to as "fast images". There are 756 and 714 fast images from ImageNet and ImageNet V2 respectively, out of the total 2000 images used for evaluation. Accuracies of models and humans on the fast images increased significantly, especially for humans. <br />
<br />
This result suggests that human labelers know when an image is difficult to label and would spend more time on it. It also shows that the models are more likely to correctly label images that human labelers can label relatively quickly.<br />
<br />
== Related Work ==<br />
<br />
=== Human accuracy on ImageNet ===<br />
<br />
Russakovsky et al. (2015) studied two trained human labelers' accuracies on 1500 and 258 images in the context of the ImageNet challenge. The top-5 accuracy of the labeler who labeled 1500 images was the well-known human baseline on ImageNet. <br />
<br />
As introduced before, the researchers went beyond by using multi-label accuracy, using more labelers, and focusing on robustness to small distribution shifts. Although the researchers had some different findings, some results are also consistent with results from (Russakovsky et al., 2015). An example is that both experiments indicated that it takes human labelers around one minute to label an image. The time distribution also has a long tail, due to the difficult images as mentioned before.<br />
<br />
=== Human performance in computer vision broadly ===<br />
There are many examples of recent studies about humans in the area of computer vision, such as investigating human robustness to synthetic distribution change (Geirhos et al., 2017) and studying what characteristics do humans use to recognize objects (Geirhos et al., 2018). Other examples include the adversarial examples constructed to fool both machines and time-limited humans (Elsayed et al., 2018) and illustrating foreground/background objects' effects on human and machine performance (Zhu et al., 2016). <br />
<br />
=== Multi-label annotations ===<br />
Stock & Cissé (2017) also studied ImageNet's multi-label nature, which aligns with the researchers' study in this paper. According to Stock & Cissé (2017), the top-1 accuracy measure could underestimate multi-label by up to 13.2%.<br />
<br />
=== ImageNet inconsistencies and label error ===<br />
Researches have found and recorded some incorrectly labeled images from ImageNet and ImageNet V2 during this study. Earlier studies (Van Horn et al., 2015) also shown that at least 4% of the birds in ImageNet are misclassified. This work also noted that the inconsistent taxonomic structure in birds' classes could lead to weak class boundaries. Researchers also noted that the majority of the fine-grained organism classes also had similar taxonomic issues.<br />
<br />
=== Distribution shift ===<br />
There has been an increasing amount of studies in this area. One focus of the studies is distributionally robust optimization (DRO), which finds the model that has the smallest worst-case expected error over a set of probability distributions. Another focus is on finding the model with the lowest error rates on adversarial examples. Work in both areas has been productive, but none was shown to resolve the drop in accuracies between ImageNet and ImageNet V2. A recent [https://papers.nips.cc/paper/2019/file/8558cb408c1d76621371888657d2eb1d-Paper.pdf paper] also discusses quantifying uncertainty under a distribution shift, in other words whether the output of probabilistic deep learning models should or should not be trusted.<br />
<br />
== Conclusion and Future Work ==<br />
<br />
=== Conclusion ===<br />
Researchers noted that in order to achieve truly reliable machine learning, researchers need a deeper understanding of the range of parameters where the model still remain robust. Techniques from Combinatorics and sensitivity analysis, in particular, might yield fruitful results. This study has provided valuable insights into the desired robustness properties by comparing model performance to human performance. This is especially evident given the results of the experiment which show humans drastically outperforming machine learning in many cases and proposes the question of how much accuracy one is willing to give up in exchange for efficiency. The results have shown that current performance benchmarks are not addressing the robustness to small and natural distribution shifts, which are easily handled by humans.<br />
<br />
=== Future work ===<br />
Other than improving the robustness of models, researchers should consider investigating if less-trained human labelers can achieve a similar level of robustness to distributional shifts. In addition, researchers can study the robustness to temporal changes, which is another form of natural distribution shift (Gu et al., 2019; Shankar et al., 2019). Also, Convolutional Neural Network can be a candidate to improve the accuracy of classifying images.<br />
<br />
== Critiques ==<br />
<br />
# Table 1 simply showed a difference in ImageNet multi-label accuracy yet does not give an explicit reason as to why such a difference is present. Although the paper suggested the distribution shift has caused the difference, it does not give other factors to concretely explain why the distribution shift was the cause.<br />
# With the recommendation to future machine evaluations, the paper proposed to "Report performances on dogs, other animals, and inanimate objects separately.". Despite its intentions, it is narrowly specific and requires further generalization for it to be convincing. <br />
# With choosing human subjects as samplers, no further information was given as to how they are chosen nor there are any background information was given. As it is a classification problem involving many classes as specific to species, a biology student would give far more accurate results than a computer science student or a math student. <br />
# As explaining the importance of multi-label metrics using comparison to Top-5 metric, the turtle example falls within the overall similarity (simony) classification of the multi-label evaluation metric, as such, if the Top-5 evaluation suggests any one of the turtle species were selected, the algorithm is considered to produce a correct prediction which is the intention. The example does not convey the necessity of changing to the proposed metric over the Top-5 metric. <br />
# With the definition in the paper regarding multi-label metrics, it is hard to see why expanding the label set is different from a traditional Top-5 metric or rather necessary, ergo does not yield the claim which the proposed metric is necessary for rigorous accuracy evaluation on ImageNet.<br />
# When discussing the main results, the paper discusses the hypothesis on distribution shift having no effects on human and machine model accuracies; the presentation is poor at best with no clear centric to what they are trying to convey to how (in detail) they resulted in such claims.<br />
# In the experiment setup of the presentation, there are a lot of key terms without detailed description. For example, Human labeler training using a subset of the remaining 30,000 unannotated images in the ImageNet validation set, labelers A, B, C, D, and E underwent extensive training to understand the intricacies of fine-grained class distinctions in the ImageNet class hierarchy. Authors should clarify each key term in the presentation otherwise readers are hard to follow.<br />
# Not sure how the human samplers were determined and simply picking several people will have really high bias because the sample is too small and they have different background which will definitely affect the results a lot. Also, it will be better if there are more comparisons between the model introduced and other models.<br />
# Given the low amount of human participants, it is hard to take the results seriously (there is too much variance). Also it's not exactly clear how the authors determined that the multi-label accuracy metric measures a semantically more meaningful notion of accuracy compared to its counterparts. For example, one of the issues with top-5 accuracy that they mention is: "For instance, within the dataset, five turtle classes are given which is difficult to distinguish under such classification evaluations." But it's not clear how multi-label accuracy would be better in this instance.<br />
# It is unclear how well the human labeler can perform labeling after training. So the final result is not that trust-worthy.<br />
# In this experiment set up, label annotators are the same as participants of the experiments. Even if there's a break between the annotating and evaluating human labeler evaluation, the impact of the break in reducing bias is not clear. One potential human labeling data is google's "I'm not a robot" verification test. One variation of the verification test asks users to select all the photos from 9 images that are related to a certain keyword. This allows for a more accurate measurement of human performance vs ImageNet performance. In addition, it's going to reduce the biases from the small number of experiment participants.<br />
# Following Table 2, the authors appear to try and claim that the model is better than the human labelers, simply because the model experienced a better increase in classification following the removal of dog photos then the human labeler did, however, a quick look at the table shows that most human labelers still performed better than the best model. The authors should be making the claim that human labelers are better at labeling dogs than the modal, but are still better overall after removing the dogs dataset.<br />
# The reason why human labeler outperforms CNN could be human had much more training. It would be more convincing if the paper could provide a metric in order to measure human labelers' training data set size.</div>Y492zhuhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Evaluating_Machine_Accuracy_on_ImageNet&diff=49170Evaluating Machine Accuracy on ImageNet2020-12-05T02:53:34Z<p>Y492zhu: /* Critiques */</p>
<hr />
<div>== Presented by == <br />
Siyuan Xia, Jiaxiang Liu, Jiabao Dong, Yipeng Du<br />
<br />
== Introduction == <br />
ImageNet is the most influential data set in machine learning with images and corresponding labels over 1000 classes. This paper intends to explore the causes for performance differences between human experts and machine learning models, more specifically, CNN, on ImageNet. <br />
<br />
Firstly, some images could belong to multiple classes. As a result, it is possible to underestimate the performance if we assign each image with only one label, which is what is being done in the top-1 metric. Therefore, we adopt both top-1 and top-5 metrics where the performances of models, unlike human labelers, are linearly correlated in both cases.<br />
<br />
Secondly, in contrast to the uniform performance of models in classes, humans tend to achieve better performances on inanimate objects. Human labelers achieve similar overall accuracies as the models, which indicates spaces of improvements on specific classes for machines.<br />
<br />
Lastly, the setup of drawing training and test sets from the same distribution may favor models over human labelers. That is, the accuracy of multi-class prediction from models drops when the testing set is drawn from a different distribution than the training set, ImageNetV2. But this shift in distribution does not cause a problem for human labelers.<br />
<br />
== Experiment Setup ==<br />
=== Overview ===<br />
There are four main phases to the experiment, which are (i) initial multilabel annotation, (ii) human labeler training, (iii) human labeler evaluation, and (iv) final annotation overview. The five authors of the paper are the participants in the experiments. <br />
<br />
A brief overview of the four phases is as follows:<br />
[[File:Experiment Set Up.png |800px| center]]<br />
<br />
=== Initial multi-label annotation ===<br />
Three labelers A, B, and C provided multi-label annotations for a subset from the ImageNet validation set, and all images from the ImageNetV2 test sets. These experiences give A, B, and C extensive experience with the ImageNet dataset. <br />
<br />
=== Human Labeler Training === <br />
All five labelers trained on labeling a subset of the remaining ImageNet images. "Training" the human labelers consisted of teaching the humans the distinctions between very similar classes in the training set. For example, there are 118 classes of "dog" within ImageNet and typical human participants will not have working knowledge of the names of each breed of dog seen even if they can recognize and distinguish that breed from others.<br />
<br />
=== Human Labeler Evaluation ===<br />
Class-balanced random samples, which contains 1,000 images from the 20,000 annotated images are generated from both the ImageNet validation set and ImageNetV2. Five participants labeled these images over 28 days.<br />
<br />
=== Final annotation Review ===<br />
All labelers reviewed the additional annotations generated in the human labeler evaluation phase.<br />
<br />
== Multi-label annotations==<br />
[[File:Categories Multilabel.png|800px|center]]<br />
<div align="center">Figure 3</div><br />
<br />
===Top-1 accuracy===<br />
With Top-1 accuracy being the standard accuracy measure used in classification studies, it measures the proportions of examples for which the predicted label matches the single target label. As many images often contain more than one object for classification, for example, Figure 3a contains a desk, laptop, keyboard, space bar, and more. With Figure 3b showing a centered prominent figure yet labeled otherwise (people vs picket fence), it can be seen how a single target label is inaccurate for such a task since identifying the main objects in the image does not suffice due to its overly stringent and punishes predictions that are the main image yet does not match its label.<br />
===Top-5 accuracy===<br />
With Top-5 considers a classification correct if the object label is in the top 5 predicted labels, it partially resolves the problem with Top-1 labeling yet it is still not ideal since it can trivialize class distinctions. For instance, within the dataset, five turtle classes are given which is difficult to distinguish under such classification evaluations.<br />
===Multi-label accuracy===<br />
The paper then proposes that for every image, the image shall have a set of target labels and a prediction; if such prediction matches one of the labels, it will be considered as correct labeling. Due to the above-discussed limitations of Top-1 and Top-5 metrics, the paper claims it is necessary for rigorous accuracy evaluation on the dataset. <br />
<br />
===Types of Multi-label annotations===<br />
====Multiple objects or organisms====<br />
For the images containing more than one object or organism that corresponds to ImageNet, the paper proposed to add an additional target label for each entity in the image. With the discussed image in Figure 3b, the class groom, bow tie, suit, gown, and hoopskirt are all present in the foreground which is then subsequently added to the set of labels.<br />
====Synonym or subset relations====<br />
For similar classes, the paper considers them as under the same bigger class, that is, for two similarly labeled images, classification is considered correct if the produced label matches either one of the labels. For instance, warthog, African elephant, and Indian element all have prominent tusks, they will be considered subclasses of the tusker, Figure 3c shows a modification of labels to contain tusker as a correct label.<br />
====Unclear Image====<br />
In certain cases such as Figure 3d, there is a distinctive difficulty to determine whether a label was correct due to ambiguities in the class hierarchy.<br />
===Collecting multi-label annotations===<br />
Participants reviewed all predictions made by the models on the dataset ImageNet and ImageNet-V2, the participants then categorized every unique prediction made by the models on the dataset into correct and incorrect labels in order to allow all images to have multiple correct labels to satisfy the above-listed method.<br />
===The multi-label accuracy metric===<br />
One prediction is only correct if and only if it was marked correct by the expert reviewers during the annotation stage. As discussed in the experiment setup section, after human labelers have completed labeling, a second annotation stage is conducted. In Figure 4, a comparison of Top-1, Top-5, and multi-label accuracies showed higher Top-1 and Top-5 accuracy corresponds with higher multi-label accuracy as expected. With multi-label accuracies measures consistently higher than Top-1 yet lower than Top-5 which shows a high correlation between the three metrics, the paper concludes that multi-label metrics measures a semantically more meaningful notion of accuracy compared to its counterparts.<br />
<br />
== Human Accuracy Measurement Process ==<br />
=== Bias Control ===<br />
Since three participants participated in the initial round of annotation, they did not look at the data for six months, and two additional annotators are introduced in the final evaluation phase to ensure fairness of the experiment. <br />
<br />
=== Human Labeler Training ===<br />
The three main difficulties encountered during human labeler training are fine-grained distinctions, class unawareness, and insufficient training images. Thus, three training regimens are provided to address the problems listed above, respectively. First, labelers will be assigned extra training tasks with immediate feedbacks on similar classes. Second, labelers will be provided access to search for specific classes during labeling. Finally, the training set will contain a reasonable amount of images for each class.<br />
<br />
=== Labeling Guide ===<br />
A labeling guide is constructed to distill class analysis learned during training into discriminative traits that could be used as a reference during the final labeling evaluation.<br />
<br />
=== Final Evaluation and Review ===<br />
Two samples, each containing 1000 images, are sampled from ImageNet and ImageNetV2, respectively, They are sampled in a class-balanced manner and shuffled together. Over 28 days, all five participants labeled all images. They spent a median of 26 seconds per image. After labeling is completed, an additional multi-label annotation session was conducted, in which human predictions for all images are manually reviewed. Comparing to the initial round of labeling, 37% of the labels changes due to participants' greater familiarity with the classes.<br />
<br />
== Main Results ==<br />
[[File:Evaluating Machine Accuracy on ImageNet Figure 1.png | center]]<br />
<br />
<div align="center">Figure 1</div><br />
<br />
===Comparison of Human and Machine Accuracies on Image Net===<br />
From Figure 1, we can see that the difference in accuracies between the datasets is within 1% for all human participants. As hypothesized, human testers indeed performed better than the automated models on both datasets. It's worth noticing that labelers D and E, who did not participate in the initial annotation period, actually performed better than the best automated model.<br />
===Comparison of Human and Machine Accuracies on Image Net===<br />
Based on the results shown in Figure 1, we can see that the confidence interval of the best 4 human participants and 4 best model overlap; however, with a p-value of 0.037 using the McNemar's paired test, it rejects the hypothesis that the FixResNeXt model and Human E labeler have the same accuracy with respect to the ImageNet validation dataset. Figure 1 also shows that the confidence intervals of the labeling accuracies for human labelers C, D, E do not overlap with the confidence interval of the best model with respect to ImageNet-V2 and with the McNemar's test yielding a p-value of <math>2\times 10^{-4}</math>, it is clear that the hypothesis human and machined models have same robustness to model distribution shifts ought to be rejected.<br />
<br />
== Other Observations ==<br />
<br />
[[File: Results_Summary_Table.png| 800px|center]]<br />
<br />
=== Difficult Images ===<br />
<br />
The experiment also shed some light on images that are difficult to label. 10 images were misclassified by all of the human labelers. Among those 10 images, there was 1 image of a monkey and 9 of dogs. In addition, 27 images, with 19 in object classes and 8 in organism classes, were misclassified by all 72 machine learning models in this experiment. Only 2 images were labeled wrong by all human labelers and models. Both images contained dogs. Researchers also noted that difficult images for models are mostly images of objects and exclusively images of animals for human labelers.<br />
<br />
=== Accuracies without dogs ===<br />
<br />
As previously discussed in the paper, machine learning models tend to outperform human labelers when classifying the 118 dog classes. To better understand to what extent does models outperform human labelers, researchers computed the accuracies again by excluding all the dog classes. Results showed a 0.6% increase in accuracy on the ImageNet images using the best model and a 1.1% increase on the ImageNet V2 images. In comparison, the mean increases in accuracy for human labelers are 1.9% and 1.8% on the ImageNet and ImageNet V2 images respectively. Researchers also conducted a simulation to demonstrate that the increase in human labeling accuracy on non-dog images is significant. This simulation was done by bootstrapping to estimate the changes in accuracy when only using data for the non-dog classes, and simulation results show smaller increases than in the experiment. <br />
<br />
In conclusion, it's more difficult for human labelers to classify images with dogs than it is for machine learning models.<br />
<br />
=== Accuracies on objects ===<br />
Researchers also computed machine and human labelers' accuracies on a subset of data with only objects, as opposed to organisms, to better illustrate the differences in performance. This test involved 590 object classes. As shown in the table above, there is a 3.3% and 3.4% increase in mean accuracies for human labelers on the ImageNet and ImageNet V2 images. In contrast, there is a 0.5% decrease in accuracy for the best model on both ImageNet and ImageNet V2. This indicates that human labelers are much better at classifying objects than these models are.<br />
<br />
=== Accuracies on fast images ===<br />
Unlike the CNN models, human labelers spent different amounts of time on different images, spanning from several seconds to 40 minutes. To further analyze the images that take human labelers less time to classify, researchers took a subset of images with median labeling time spent by human labelers of at most 60 seconds. These images were referred to as "fast images". There are 756 and 714 fast images from ImageNet and ImageNet V2 respectively, out of the total 2000 images used for evaluation. Accuracies of models and humans on the fast images increased significantly, especially for humans. <br />
<br />
This result suggests that human labelers know when an image is difficult to label and would spend more time on it. It also shows that the models are more likely to correctly label images that human labelers can label relatively quickly.<br />
<br />
== Related Work ==<br />
<br />
=== Human accuracy on ImageNet ===<br />
<br />
Russakovsky et al. (2015) studied two trained human labelers' accuracies on 1500 and 258 images in the context of the ImageNet challenge. The top-5 accuracy of the labeler who labeled 1500 images was the well-known human baseline on ImageNet. <br />
<br />
As introduced before, the researchers went beyond by using multi-label accuracy, using more labelers, and focusing on robustness to small distribution shifts. Although the researchers had some different findings, some results are also consistent with results from (Russakovsky et al., 2015). An example is that both experiments indicated that it takes human labelers around one minute to label an image. The time distribution also has a long tail, due to the difficult images as mentioned before.<br />
<br />
=== Human performance in computer vision broadly ===<br />
There are many examples of recent studies about humans in the area of computer vision, such as investigating human robustness to synthetic distribution change (Geirhos et al., 2017) and studying what characteristics do humans use to recognize objects (Geirhos et al., 2018). Other examples include the adversarial examples constructed to fool both machines and time-limited humans (Elsayed et al., 2018) and illustrating foreground/background objects' effects on human and machine performance (Zhu et al., 2016). <br />
<br />
=== Multi-label annotations ===<br />
Stock & Cissé (2017) also studied ImageNet's multi-label nature, which aligns with the researchers' study in this paper. According to Stock & Cissé (2017), the top-1 accuracy measure could underestimate multi-label by up to 13.2%.<br />
<br />
=== ImageNet inconsistencies and label error ===<br />
Researches have found and recorded some incorrectly labeled images from ImageNet and ImageNet V2 during this study. Earlier studies (Van Horn et al., 2015) also shown that at least 4% of the birds in ImageNet are misclassified. This work also noted that the inconsistent taxonomic structure in birds' classes could lead to weak class boundaries. Researchers also noted that the majority of the fine-grained organism classes also had similar taxonomic issues.<br />
<br />
=== Distribution shift ===<br />
There has been an increasing amount of studies in this area. One focus of the studies is distributionally robust optimization (DRO), which finds the model that has the smallest worst-case expected error over a set of probability distributions. Another focus is on finding the model with the lowest error rates on adversarial examples. Work in both areas has been productive, but none was shown to resolve the drop in accuracies between ImageNet and ImageNet V2. A recent [https://papers.nips.cc/paper/2019/file/8558cb408c1d76621371888657d2eb1d-Paper.pdf paper] also discusses quantifying uncertainty under a distribution shift, in other words whether the output of probabilistic deep learning models should or should not be trusted.<br />
<br />
== Conclusion and Future Work ==<br />
<br />
=== Conclusion ===<br />
Researchers noted that in order to achieve truly reliable machine learning, researchers need a deeper understanding of the range of parameters where the model still remain robust. Techniques from Combinatorics and sensitivity analysis, in particular, might yield fruitful results. This study has provided valuable insights into the desired robustness properties by comparing model performance to human performance. This is especially evident given the results of the experiment which show humans drastically outperforming machine learning in many cases and proposes the question of how much accuracy one is willing to give up in exchange for efficiency. The results have shown that current performance benchmarks are not addressing the robustness to small and natural distribution shifts, which are easily handled by humans.<br />
<br />
=== Future work ===<br />
Other than improving the robustness of models, researchers should consider investigating if less-trained human labelers can achieve a similar level of robustness to distributional shifts. In addition, researchers can study the robustness to temporal changes, which is another form of natural distribution shift (Gu et al., 2019; Shankar et al., 2019). Also, Convolutional Neural Network can be a candidate to improve the accuracy of classifying images.<br />
<br />
== Critiques ==<br />
<br />
# Table 1 simply showed a difference in ImageNet multi-label accuracy yet does not give an explicit reason as to why such a difference is present. Although the paper suggested the distribution shift has caused the difference, it does not give other factors to concretely explain why the distribution shift was the cause.<br />
# With the recommendation to future machine evaluations, the paper proposed to "Report performances on dogs, other animals, and inanimate objects separately.". Despite its intentions, it is narrowly specific and requires further generalization for it to be convincing. <br />
# With choosing human subjects as samplers, no further information was given as to how they are chosen nor there are any background information was given. As it is a classification problem involving many classes as specific to species, a biology student would give far more accurate results than a computer science student or a math student. <br />
# As explaining the importance of multi-label metrics using comparison to Top-5 metric, the turtle example falls within the overall similarity (simony) classification of the multi-label evaluation metric, as such, if the Top-5 evaluation suggests any one of the turtle species were selected, the algorithm is considered to produce a correct prediction which is the intention. The example does not convey the necessity of changing to the proposed metric over the Top-5 metric. <br />
# With the definition in the paper regarding multi-label metrics, it is hard to see why expanding the label set is different from a traditional Top-5 metric or rather necessary, ergo does not yield the claim which the proposed metric is necessary for rigorous accuracy evaluation on ImageNet.<br />
# When discussing the main results, the paper discusses the hypothesis on distribution shift having no effects on human and machine model accuracies; the presentation is poor at best with no clear centric to what they are trying to convey to how (in detail) they resulted in such claims.<br />
# In the experiment setup of the presentation, there are a lot of key terms without detailed description. For example, Human labeler training using a subset of the remaining 30,000 unannotated images in the ImageNet validation set, labelers A, B, C, D, and E underwent extensive training to understand the intricacies of fine-grained class distinctions in the ImageNet class hierarchy. Authors should clarify each key term in the presentation otherwise readers are hard to follow.<br />
# Not sure how the human samplers were determined and simply picking several people will have really high bias because the sample is too small and they have different background which will definitely affect the results a lot. Also, it will be better if there are more comparisons between the model introduced and other models.<br />
# Given the low amount of human participants, it is hard to take the results seriously (there is too much variance). Also it's not exactly clear how the authors determined that the multi-label accuracy metric measures a semantically more meaningful notion of accuracy compared to its counterparts. For example, one of the issues with top-5 accuracy that they mention is: "For instance, within the dataset, five turtle classes are given which is difficult to distinguish under such classification evaluations." But it's not clear how multi-label accuracy would be better in this instance.<br />
# It is unclear how well the human labeler can perform labeling after training. So the final result is not that trust-worthy.<br />
# In this experiment set up, label annotators are the same as participants of the experiments. Even if there's a break between the annotating and evaluating human labeler evaluation, the impact of the break in reducing bias is not clear. One potential human labeling data is google's "I'm not a robot" verification test. One variation of the verification test asks users to select all the photos from 9 images that are related to a certain keyword. This allows for a more accurate measurement of human performance vs ImageNet performance. In addition, it's going to reduce the biases from the small number of experiment participants.<br />
# Following Table 2, the authors appear to try and claim that the model is better than the human labelers, simply because the model experienced a better increase in classification following the removal of dog photos then the human labeler did, however, a quick look at the table shows that most human labelers still performed better than the best model. The authors should be making the claim that human labelers are better at labeling dogs than the modal, but are still better overall after removing the dogs dataset.<br />
# The reason why human labeler outperforms CNN could be human had much more training. It would be more convincing if the paper could provide a metric in order to measure human labeler's training data set size.</div>Y492zhuhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Evaluating_Machine_Accuracy_on_ImageNet&diff=49169Evaluating Machine Accuracy on ImageNet2020-12-05T02:40:05Z<p>Y492zhu: /* Introduction */</p>
<hr />
<div>== Presented by == <br />
Siyuan Xia, Jiaxiang Liu, Jiabao Dong, Yipeng Du<br />
<br />
== Introduction == <br />
ImageNet is the most influential data set in machine learning with images and corresponding labels over 1000 classes. This paper intends to explore the causes for performance differences between human experts and machine learning models, more specifically, CNN, on ImageNet. <br />
<br />
Firstly, some images could belong to multiple classes. As a result, it is possible to underestimate the performance if we assign each image with only one label, which is what is being done in the top-1 metric. Therefore, we adopt both top-1 and top-5 metrics where the performances of models, unlike human labelers, are linearly correlated in both cases.<br />
<br />
Secondly, in contrast to the uniform performance of models in classes, humans tend to achieve better performances on inanimate objects. Human labelers achieve similar overall accuracies as the models, which indicates spaces of improvements on specific classes for machines.<br />
<br />
Lastly, the setup of drawing training and test sets from the same distribution may favor models over human labelers. That is, the accuracy of multi-class prediction from models drops when the testing set is drawn from a different distribution than the training set, ImageNetV2. But this shift in distribution does not cause a problem for human labelers.<br />
<br />
== Experiment Setup ==<br />
=== Overview ===<br />
There are four main phases to the experiment, which are (i) initial multilabel annotation, (ii) human labeler training, (iii) human labeler evaluation, and (iv) final annotation overview. The five authors of the paper are the participants in the experiments. <br />
<br />
A brief overview of the four phases is as follows:<br />
[[File:Experiment Set Up.png |800px| center]]<br />
<br />
=== Initial multi-label annotation ===<br />
Three labelers A, B, and C provided multi-label annotations for a subset from the ImageNet validation set, and all images from the ImageNetV2 test sets. These experiences give A, B, and C extensive experience with the ImageNet dataset. <br />
<br />
=== Human Labeler Training === <br />
All five labelers trained on labeling a subset of the remaining ImageNet images. "Training" the human labelers consisted of teaching the humans the distinctions between very similar classes in the training set. For example, there are 118 classes of "dog" within ImageNet and typical human participants will not have working knowledge of the names of each breed of dog seen even if they can recognize and distinguish that breed from others.<br />
<br />
=== Human Labeler Evaluation ===<br />
Class-balanced random samples, which contains 1,000 images from the 20,000 annotated images are generated from both the ImageNet validation set and ImageNetV2. Five participants labeled these images over 28 days.<br />
<br />
=== Final annotation Review ===<br />
All labelers reviewed the additional annotations generated in the human labeler evaluation phase.<br />
<br />
== Multi-label annotations==<br />
[[File:Categories Multilabel.png|800px|center]]<br />
<div align="center">Figure 3</div><br />
<br />
===Top-1 accuracy===<br />
With Top-1 accuracy being the standard accuracy measure used in classification studies, it measures the proportions of examples for which the predicted label matches the single target label. As many images often contain more than one object for classification, for example, Figure 3a contains a desk, laptop, keyboard, space bar, and more. With Figure 3b showing a centered prominent figure yet labeled otherwise (people vs picket fence), it can be seen how a single target label is inaccurate for such a task since identifying the main objects in the image does not suffice due to its overly stringent and punishes predictions that are the main image yet does not match its label.<br />
===Top-5 accuracy===<br />
With Top-5 considers a classification correct if the object label is in the top 5 predicted labels, it partially resolves the problem with Top-1 labeling yet it is still not ideal since it can trivialize class distinctions. For instance, within the dataset, five turtle classes are given which is difficult to distinguish under such classification evaluations.<br />
===Multi-label accuracy===<br />
The paper then proposes that for every image, the image shall have a set of target labels and a prediction; if such prediction matches one of the labels, it will be considered as correct labeling. Due to the above-discussed limitations of Top-1 and Top-5 metrics, the paper claims it is necessary for rigorous accuracy evaluation on the dataset. <br />
<br />
===Types of Multi-label annotations===<br />
====Multiple objects or organisms====<br />
For the images containing more than one object or organism that corresponds to ImageNet, the paper proposed to add an additional target label for each entity in the image. With the discussed image in Figure 3b, the class groom, bow tie, suit, gown, and hoopskirt are all present in the foreground which is then subsequently added to the set of labels.<br />
====Synonym or subset relations====<br />
For similar classes, the paper considers them as under the same bigger class, that is, for two similarly labeled images, classification is considered correct if the produced label matches either one of the labels. For instance, warthog, African elephant, and Indian element all have prominent tusks, they will be considered subclasses of the tusker, Figure 3c shows a modification of labels to contain tusker as a correct label.<br />
====Unclear Image====<br />
In certain cases such as Figure 3d, there is a distinctive difficulty to determine whether a label was correct due to ambiguities in the class hierarchy.<br />
===Collecting multi-label annotations===<br />
Participants reviewed all predictions made by the models on the dataset ImageNet and ImageNet-V2, the participants then categorized every unique prediction made by the models on the dataset into correct and incorrect labels in order to allow all images to have multiple correct labels to satisfy the above-listed method.<br />
===The multi-label accuracy metric===<br />
One prediction is only correct if and only if it was marked correct by the expert reviewers during the annotation stage. As discussed in the experiment setup section, after human labelers have completed labeling, a second annotation stage is conducted. In Figure 4, a comparison of Top-1, Top-5, and multi-label accuracies showed higher Top-1 and Top-5 accuracy corresponds with higher multi-label accuracy as expected. With multi-label accuracies measures consistently higher than Top-1 yet lower than Top-5 which shows a high correlation between the three metrics, the paper concludes that multi-label metrics measures a semantically more meaningful notion of accuracy compared to its counterparts.<br />
<br />
== Human Accuracy Measurement Process ==<br />
=== Bias Control ===<br />
Since three participants participated in the initial round of annotation, they did not look at the data for six months, and two additional annotators are introduced in the final evaluation phase to ensure fairness of the experiment. <br />
<br />
=== Human Labeler Training ===<br />
The three main difficulties encountered during human labeler training are fine-grained distinctions, class unawareness, and insufficient training images. Thus, three training regimens are provided to address the problems listed above, respectively. First, labelers will be assigned extra training tasks with immediate feedbacks on similar classes. Second, labelers will be provided access to search for specific classes during labeling. Finally, the training set will contain a reasonable amount of images for each class.<br />
<br />
=== Labeling Guide ===<br />
A labeling guide is constructed to distill class analysis learned during training into discriminative traits that could be used as a reference during the final labeling evaluation.<br />
<br />
=== Final Evaluation and Review ===<br />
Two samples, each containing 1000 images, are sampled from ImageNet and ImageNetV2, respectively, They are sampled in a class-balanced manner and shuffled together. Over 28 days, all five participants labeled all images. They spent a median of 26 seconds per image. After labeling is completed, an additional multi-label annotation session was conducted, in which human predictions for all images are manually reviewed. Comparing to the initial round of labeling, 37% of the labels changes due to participants' greater familiarity with the classes.<br />
<br />
== Main Results ==<br />
[[File:Evaluating Machine Accuracy on ImageNet Figure 1.png | center]]<br />
<br />
<div align="center">Figure 1</div><br />
<br />
===Comparison of Human and Machine Accuracies on Image Net===<br />
From Figure 1, we can see that the difference in accuracies between the datasets is within 1% for all human participants. As hypothesized, human testers indeed performed better than the automated models on both datasets. It's worth noticing that labelers D and E, who did not participate in the initial annotation period, actually performed better than the best automated model.<br />
===Comparison of Human and Machine Accuracies on Image Net===<br />
Based on the results shown in Figure 1, we can see that the confidence interval of the best 4 human participants and 4 best model overlap; however, with a p-value of 0.037 using the McNemar's paired test, it rejects the hypothesis that the FixResNeXt model and Human E labeler have the same accuracy with respect to the ImageNet validation dataset. Figure 1 also shows that the confidence intervals of the labeling accuracies for human labelers C, D, E do not overlap with the confidence interval of the best model with respect to ImageNet-V2 and with the McNemar's test yielding a p-value of <math>2\times 10^{-4}</math>, it is clear that the hypothesis human and machined models have same robustness to model distribution shifts ought to be rejected.<br />
<br />
== Other Observations ==<br />
<br />
[[File: Results_Summary_Table.png| 800px|center]]<br />
<br />
=== Difficult Images ===<br />
<br />
The experiment also shed some light on images that are difficult to label. 10 images were misclassified by all of the human labelers. Among those 10 images, there was 1 image of a monkey and 9 of dogs. In addition, 27 images, with 19 in object classes and 8 in organism classes, were misclassified by all 72 machine learning models in this experiment. Only 2 images were labeled wrong by all human labelers and models. Both images contained dogs. Researchers also noted that difficult images for models are mostly images of objects and exclusively images of animals for human labelers.<br />
<br />
=== Accuracies without dogs ===<br />
<br />
As previously discussed in the paper, machine learning models tend to outperform human labelers when classifying the 118 dog classes. To better understand to what extent does models outperform human labelers, researchers computed the accuracies again by excluding all the dog classes. Results showed a 0.6% increase in accuracy on the ImageNet images using the best model and a 1.1% increase on the ImageNet V2 images. In comparison, the mean increases in accuracy for human labelers are 1.9% and 1.8% on the ImageNet and ImageNet V2 images respectively. Researchers also conducted a simulation to demonstrate that the increase in human labeling accuracy on non-dog images is significant. This simulation was done by bootstrapping to estimate the changes in accuracy when only using data for the non-dog classes, and simulation results show smaller increases than in the experiment. <br />
<br />
In conclusion, it's more difficult for human labelers to classify images with dogs than it is for machine learning models.<br />
<br />
=== Accuracies on objects ===<br />
Researchers also computed machine and human labelers' accuracies on a subset of data with only objects, as opposed to organisms, to better illustrate the differences in performance. This test involved 590 object classes. As shown in the table above, there is a 3.3% and 3.4% increase in mean accuracies for human labelers on the ImageNet and ImageNet V2 images. In contrast, there is a 0.5% decrease in accuracy for the best model on both ImageNet and ImageNet V2. This indicates that human labelers are much better at classifying objects than these models are.<br />
<br />
=== Accuracies on fast images ===<br />
Unlike the CNN models, human labelers spent different amounts of time on different images, spanning from several seconds to 40 minutes. To further analyze the images that take human labelers less time to classify, researchers took a subset of images with median labeling time spent by human labelers of at most 60 seconds. These images were referred to as "fast images". There are 756 and 714 fast images from ImageNet and ImageNet V2 respectively, out of the total 2000 images used for evaluation. Accuracies of models and humans on the fast images increased significantly, especially for humans. <br />
<br />
This result suggests that human labelers know when an image is difficult to label and would spend more time on it. It also shows that the models are more likely to correctly label images that human labelers can label relatively quickly.<br />
<br />
== Related Work ==<br />
<br />
=== Human accuracy on ImageNet ===<br />
<br />
Russakovsky et al. (2015) studied two trained human labelers' accuracies on 1500 and 258 images in the context of the ImageNet challenge. The top-5 accuracy of the labeler who labeled 1500 images was the well-known human baseline on ImageNet. <br />
<br />
As introduced before, the researchers went beyond by using multi-label accuracy, using more labelers, and focusing on robustness to small distribution shifts. Although the researchers had some different findings, some results are also consistent with results from (Russakovsky et al., 2015). An example is that both experiments indicated that it takes human labelers around one minute to label an image. The time distribution also has a long tail, due to the difficult images as mentioned before.<br />
<br />
=== Human performance in computer vision broadly ===<br />
There are many examples of recent studies about humans in the area of computer vision, such as investigating human robustness to synthetic distribution change (Geirhos et al., 2017) and studying what characteristics do humans use to recognize objects (Geirhos et al., 2018). Other examples include the adversarial examples constructed to fool both machines and time-limited humans (Elsayed et al., 2018) and illustrating foreground/background objects' effects on human and machine performance (Zhu et al., 2016). <br />
<br />
=== Multi-label annotations ===<br />
Stock & Cissé (2017) also studied ImageNet's multi-label nature, which aligns with the researchers' study in this paper. According to Stock & Cissé (2017), the top-1 accuracy measure could underestimate multi-label by up to 13.2%.<br />
<br />
=== ImageNet inconsistencies and label error ===<br />
Researches have found and recorded some incorrectly labeled images from ImageNet and ImageNet V2 during this study. Earlier studies (Van Horn et al., 2015) also shown that at least 4% of the birds in ImageNet are misclassified. This work also noted that the inconsistent taxonomic structure in birds' classes could lead to weak class boundaries. Researchers also noted that the majority of the fine-grained organism classes also had similar taxonomic issues.<br />
<br />
=== Distribution shift ===<br />
There has been an increasing amount of studies in this area. One focus of the studies is distributionally robust optimization (DRO), which finds the model that has the smallest worst-case expected error over a set of probability distributions. Another focus is on finding the model with the lowest error rates on adversarial examples. Work in both areas has been productive, but none was shown to resolve the drop in accuracies between ImageNet and ImageNet V2. A recent [https://papers.nips.cc/paper/2019/file/8558cb408c1d76621371888657d2eb1d-Paper.pdf paper] also discusses quantifying uncertainty under a distribution shift, in other words whether the output of probabilistic deep learning models should or should not be trusted.<br />
<br />
== Conclusion and Future Work ==<br />
<br />
=== Conclusion ===<br />
Researchers noted that in order to achieve truly reliable machine learning, researchers need a deeper understanding of the range of parameters where the model still remain robust. Techniques from Combinatorics and sensitivity analysis, in particular, might yield fruitful results. This study has provided valuable insights into the desired robustness properties by comparing model performance to human performance. This is especially evident given the results of the experiment which show humans drastically outperforming machine learning in many cases and proposes the question of how much accuracy one is willing to give up in exchange for efficiency. The results have shown that current performance benchmarks are not addressing the robustness to small and natural distribution shifts, which are easily handled by humans.<br />
<br />
=== Future work ===<br />
Other than improving the robustness of models, researchers should consider investigating if less-trained human labelers can achieve a similar level of robustness to distributional shifts. In addition, researchers can study the robustness to temporal changes, which is another form of natural distribution shift (Gu et al., 2019; Shankar et al., 2019). Also, Convolutional Neural Network can be a candidate to improve the accuracy of classifying images.<br />
<br />
== Critiques ==<br />
<br />
# Table 1 simply showed a difference in ImageNet multi-label accuracy yet does not give an explicit reason as to why such a difference is present. Although the paper suggested the distribution shift has caused the difference, it does not give other factors to concretely explain why the distribution shift was the cause.<br />
# With the recommendation to future machine evaluations, the paper proposed to "Report performances on dogs, other animals, and inanimate objects separately.". Despite its intentions, it is narrowly specific and requires further generalization for it to be convincing. <br />
# With choosing human subjects as samplers, no further information was given as to how they are chosen nor there are any background information was given. As it is a classification problem involving many classes as specific to species, a biology student would give far more accurate results than a computer science student or a math student. <br />
# As explaining the importance of multi-label metrics using comparison to Top-5 metric, the turtle example falls within the overall similarity (simony) classification of the multi-label evaluation metric, as such, if the Top-5 evaluation suggests any one of the turtle species were selected, the algorithm is considered to produce a correct prediction which is the intention. The example does not convey the necessity of changing to the proposed metric over the Top-5 metric. <br />
# With the definition in the paper regarding multi-label metrics, it is hard to see why expanding the label set is different from a traditional Top-5 metric or rather necessary, ergo does not yield the claim which the proposed metric is necessary for rigorous accuracy evaluation on ImageNet.<br />
# When discussing the main results, the paper discusses the hypothesis on distribution shift having no effects on human and machine model accuracies; the presentation is poor at best with no clear centric to what they are trying to convey to how (in detail) they resulted in such claims.<br />
# In the experiment setup of the presentation, there are a lot of key terms without detailed description. For example, Human labeler training using a subset of the remaining 30,000 unannotated images in the ImageNet validation set, labelers A, B, C, D, and E underwent extensive training to understand the intricacies of fine-grained class distinctions in the ImageNet class hierarchy. Authors should clarify each key term in the presentation otherwise readers are hard to follow.<br />
# Not sure how the human samplers were determined and simply picking several people will have really high bias because the sample is too small and they have different background which will definitely affect the results a lot. Also, it will be better if there are more comparisons between the model introduced and other models.<br />
# Given the low amount of human participants, it is hard to take the results seriously (there is too much variance). Also it's not exactly clear how the authors determined that the multi-label accuracy metric measures a semantically more meaningful notion of accuracy compared to its counterparts. For example, one of the issues with top-5 accuracy that they mention is: "For instance, within the dataset, five turtle classes are given which is difficult to distinguish under such classification evaluations." But it's not clear how multi-label accuracy would be better in this instance.<br />
# It is unclear how well the human labeler can perform labeling after training. So the final result is not that trust-worthy.<br />
# In this experiment set up, label annotators are the same as participants of the experiments. Even if there's a break between the annotating and evaluating human labeler evaluation, the impact of the break in reducing bias is not clear. One potential human labeling data is google's "I'm not a robot" verification test. One variation of the verification test asks users to select all the photos from 9 images that are related to a certain keyword. This allows for a more accurate measurement of human performance vs ImageNet performance. In addition, it's going to reduce the biases from the small number of experiment participants.<br />
# Following Table 2, the authors appear to try and claim that the model is better than the human labelers, simply because the model experienced a better increase in classification following the removal of dog photos then the human labeler did, however, a quick look at the table shows that most human labelers still performed better than the best model. The authors should be making the claim that human labelers are better at labeling dogs than the modal, but are still better overall after removing the dogs dataset.</div>Y492zhuhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Surround_Vehicle_Motion_Prediction&diff=49168Surround Vehicle Motion Prediction2020-12-05T02:34:58Z<p>Y492zhu: /* Critiques */</p>
<hr />
<div>DROCC: '''Surround Vehicle Motion Prediction Using LSTM-RNN for Motion Planning of Autonomous Vehicles at Multi-Lane Turn Intersections'''<br />
== Presented by == <br />
Mushi Wang, Siyuan Qiu, Yan Yu<br />
<br />
== Introduction ==<br />
<br />
This paper presents a surrounding vehicle motion prediction algorithm for multi-lane turn intersections using a Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN). More specifically, it focused on the improvement of in-lane target recognition and achieving human-like acceleration decisions at multi-lane turn intersections by introducing the learning-based target motion predictor and prediction-based motion predictor. A data-driven approach for predicting the trajectory and velocity of surrounding vehicles on urban roads at multi-lane turn intersections was described. LSTM architecture, a specific kind of RNN capable of learning long-term dependencies, is designed to manage complex vehicle motions in multi-lane turn intersections. The results show that the forecaster improves the recognition time of the leading vehicle and contributes to the improvement of prediction ability.<br />
<br />
== Previous Work ==<br />
The autonomous vehicle trajectory approaches previously used motion models like Constant Velocity and Constant Acceleration. These models are linear and are only able to handle straight motions. There are curvilinear models such as Constant Turn Rate and Velocity and Constant Turn Rate and Acceleration which handle rotations and more complex motions. Together with these models, Kalman Filter is used to predicting the vehicle trajectory. Kalman filtering is a common technique used in sensor fusion for state estimation that allows the vehicle's state to be predicted while taking into account the uncertainty associated with inputs and measurements. However, the performance of the Kalman Filter in predicting multi-step problems is not that good. Recurrent Neural Network performs significantly better than it. <br />
<br />
There are 3 main challenges to achieving fully autonomous driving on urban roads, which are scene awareness, inferring other drivers’ intentions, and predicting their future motions. Researchers are developing prediction algorithms that can simulate a driver’s intuition to improve safety when autonomous vehicles and human drivers drive together. To predict driver behavior on an urban road, there are 3 categories for the motion prediction model: (1) physics-based; (2) maneuver-based; and (3) interaction-aware. Physics-based models are simple and direct, which only consider the states of prediction vehicles kinematically. The advantage is that it has minimal computational burden among the three types. However, it is impossible to consider the interactions between vehicles. Maneuver-based models consider the driver’s intention and classified them. By predicting the driver maneuver, the future trajectory can be predicted. Identifying similar behaviors in driving is able to infer different drivers' intentions which are stated to improve the prediction accuracy. However, it still an assistant to improve physics-based models. <br />
<br />
Recurrent Neural Network (RNN) is a type of approach proposed to infer driver intention in this paper. Interaction-aware models can reflect interactions between surrounding vehicles, and predict future motions of detected vehicles simultaneously as a scene. While the prediction algorithm is more complex in computation which is often used in offline simulations. As Schulz et al. indicate, interaction models are very difficult to create as "predicting complete trajectories at once is challenging, as one needs to account for multiple hypotheses and long-term interactions between multiple agents" [6].<br />
<br />
== Motivation == <br />
Research results indicate that little research has been dedicated on predicting the trajectory of intersections. Moreover, public data sets for analyzing driver behaviour at intersections are not enough, and these data sets are not easy to collect. A model is needed to predict the various movements of the target around a multi-lane turning intersection. It is very necessary to design a motion predictor that can be used for real-time traffic.<br />
<br />
<center><br />
[[ File:intersection.png |300px]]<br />
</center><br />
<br />
== Framework == <br />
The LSTM-RNN-based motion predictor comprises three parts: (1) a data encoder; (2) an LSTM-based RNN; and (3) a data decoder depicts the architecture of the surrounding target trajectory predictor. The proposed architecture uses a perception algorithm to estimate the state of surrounding vehicles, which relies on six scanners. The output predicts the state of the surrounding vehicles and is used to determine the expected longitudinal acceleration in the actual traffic at the intersection. The following image gives a visual representation of the model.<br />
<br />
<center>[[Image:Figure1_Yan.png|800px|]]</center><br />
<br />
== LSTM-RNN based motion predictor == <br />
<br />
=== Sensor Outputs ===<br />
<br />
The input of the target perceptions is from the output of the sensors. The data collected in this article uses 6 different sensors with feature fusion to detect traffic in the range up to 100m: 1) LiDAR system outputs: Relative position, heading, velocity, and box size in local coordinates; 2) Around0View Monitoring (AVM) and 3)GPS outputs: acquire lanes, road marker, global position; 4) Gateway engine outputs: precise global position in urban road environment; 5) Micro-Autobox II and 6) a MDPS are used to control and actuate the subject. All data are stored in an industrial PC.<br />
<br />
=== Data ===<br />
Multi-lane turn intersections are the target roads in this paper. The dataset was collected using a human driven Autonomous Vehicle(AV) that was equipped with sensors to track motion the vehicle's surroundings. In addition the motion sensors they used a front camera, Around-View-Monitor and GPS to acquire the lanes, road markers and global position. The data was collected in the urban roads of Gwanak-gu, Seoul, South Korea. The training model is generated from 484 tracks collected when driving through intersections in real traffic. The previous and subsequent states of a vehicle at a particular time can be extracted. After post-processing, the collected data, a total of 16,660 data samples were generated, including 11,662 training data samples, and 4,998 evaluation data samples.<br />
<br />
=== Motion predictor ===<br />
This article proposes a data-driven method to predict the future movement of surrounding vehicles based on their previous movement, which is the sequential previous motion. The motion predictor based on the LSTM-RNN architecture in this work only uses information collected from sensors on autonomous vehicles, as shown in the figure below. The contribution of the network architecture of this study is that the future state of the target vehicle is used as the input feature for predicting the field of view. <br />
<br />
<br />
<center>[[Image:Figure7b_Yan.png|500px|]]</center><br />
<br />
<br />
==== Network architecture ==== <br />
A RNN is an artificial neural network, suitable for use with sequential data. It can also be used for time-series data, where the pattern of the data depends on the time flow. Also, it can contain feedback loops that allow activations to flow alternately in the loop.<br />
An LSTM avoids the problem of vanishing gradients by making errors flow backward without a limit on the number of virtual layers. This property prevents errors from increasing or declining over time, which can make the network train improperly. The figure below shows the various layers of the LSTM-RNN and the number of units in each layer. This structure is determined by comparing the accuracy of 72 RNNs, which consist of a combination of four input sets and 18 network configurations.<br />
<br />
<center>[[Image:Figure8_Yan.png|800px|]]</center><br />
<br />
==== Input and output features ==== <br />
In order to apply the motion predictor to the AV in motion, the speed of the data collection vehicle is added to the input sequence. The input sequence consists of relative X/Y position, relative heading angle, speed of surrounding target vehicles, and speed of data collection vehicles. The output sequence is the same as the input sequence, such as relative position, heading, and speed.<br />
<br />
==== Encoder and decoder ==== <br />
In this study, the authors introduced an encoder and decoder that process the input from the sensor and the output from the RNN, respectively. The encoder normalizes each component of the input data to rescale the data to mean 0 and standard deviation 1, while the decoder denormalizes the output data to use the same parameters as in the encoder to scale it back to the actual unit. <br />
==== Sequence length ==== <br />
The sequence length of RNN input and output is another important factor to improve prediction performance. In this study, 5, 10, 15, 20, 25, and 30 steps of 100 millisecond sampling times were compared, and 15 steps showed relatively accurate results, even among candidates The observation time is very short.<br />
<br />
== Motion planning based on surrounding vehicle motion prediction == <br />
In daily driving, experienced drivers will predict possible risks based on observations of surrounding vehicles, and ensure safety by changing behaviors before the risks occur. In order to achieve a human-like motion plan, based on the model predictive control (MPC) method, a prediction-based motion planner for autonomous vehicles is designed, which takes into account the driver’s future behavior. The cost function of the motion planner is determined as follows:<br />
\begin{equation*}<br />
\begin{split}<br />
J = & \sum_{k=1}^{N_p} (x(k|t) - x_{ref}(k|t)^T) Q(x(k|t) - x_{ref}(k|t)) +\\<br />
& R \sum_{k=0}^{N_p-1} u(k|t)^2 + R_{\Delta \mu}\sum_{k=0}^{N_p-2} (u(k+1|t) - u(k|t))^2 <br />
\end{split}<br />
\end{equation*}<br />
where <math>k</math> and <math>t</math> are the prediction step index and time index, respectively; <math>x(k|t)</math> and <math>x_{ref} (k|t)</math> are the states and reference of the MPC problem, respectively; <math>x(k|t)</math> is composed of travel distance px and longitudinal velocity vx; <math>x_{ref} (k|t)</math> consists of reference travel distance <math>p_{x,ref}</math> and reference longitudinal velocity <math>v_{x,ref}</math> ; <math>u(k|t)</math> is the control input, which is the longitudinal acceleration command; <math>N_p</math> is the prediction horizon; and Q, R, and <math>R_{\Delta \mu}</math> are the weight matrices for states, input, and input derivative, respectively, and these weight matrices were tuned to obtain control inputs from the proposed controller that were as similar as possible to those of human-driven vehicles. <br />
The constraints of the control input are defined as follows:<br />
\begin{equation*}<br />
\begin{split}<br />
&\mu_{min} \leq \mu(k|t) \leq \mu_{max} \\<br />
&||\mu(k+1|t) - \mu(k|t)|| \leq S<br />
\end{split}<br />
\end{equation*}<br />
Where <math>u_{min}</math>, <math>u_{max}</math>and S are the minimum/maximum control input and maximum slew rate of input respectively.<br />
<br />
Determine the position and speed boundary based on the predicted state:<br />
\begin{equation*}<br />
\begin{split}<br />
& p_{x,max}(k|t) = p_{x,tar}(k|t) - c_{des}(k|t) \quad p_{x,min}(k|t) = 0 \\<br />
& v_{x,max}(k|t) = min(v_{x,ret}(k|t), v_{x,limit}) \quad v_{x,min}(k|t) = 0<br />
\end{split}<br />
\end{equation*}<br />
Where <math>v_{x, limit}</math> are the speed limits of the target vehicle.<br />
<br />
== Prediction performance analysis and application to motion planning ==<br />
=== Accuracy analysis ===<br />
The proposed algorithm was compared with the results from three base algorithms, a path-following model with <br />
constant velocity, a path-following model with traffic flow and a CTRV model.<br />
<br />
We compare those algorithms according to four sorts of errors, The <math>x</math> position error <math>e_{x,T_p}</math>, <br />
<math>y</math> position error <math>e_{y,T_p}</math>, heading error <math>e_{\theta,T_p}</math>, and velocity error <math>e_{v,T_p}</math> where <math>T_p</math> denotes time <math>p</math>. These four errors are defined as follows:<br />
<br />
\begin{equation*}<br />
\begin{split}<br />
e_{x,Tp}=& p_{x,Tp} -\hat {p}_{x,Tp}\\ <br />
e_{y,Tp}=& p_{y,Tp} -\hat {p}_{y,Tp}\\ <br />
e_{\theta,Tp}=& \theta _{Tp} -\hat {\theta }_{Tp}\\ <br />
e_{v,Tp}=& v_{Tp} -\hat {v}_{Tp}<br />
\end{split}<br />
\end{equation*}<br />
<center>[[Image:Figure10.1_YanYu.png|500px|]]</center><br />
<br />
The proposed model shows significantly fewer prediction errors compare to the based algorithms in terms of mean, <br />
standard deviation(STD), and root mean square error(RMSE). Meanwhile, the proposed model exhibits a bell-shaped <br />
curve with a close to zero mean, which indicates that the proposed algorithm's prediction of human divers' <br />
intensions are relatively precise. On the other hand, <math>e_{x,T_p}</math>, <math>e_{y,T_p}</math>, <math>e_{v,T_p}</math> are bounded within <br />
reasonable levels. For instant, the three-sigma range of <math>e_{y,T_p}</math> is within the width of a lane. Therefore, <br />
the proposed algorithm can be precise and maintain safety simultaneously.<br />
<br />
=== Motion planning application ===<br />
==== Case study of a multi-lane left turn scenario ====<br />
The proposed method mimics a human driver better, by simulating a human driver's decision-making process. <br />
In a multi-lane left turn scenario, the proposed algorithm correctly predicted the trajectory of a target <br />
the vehicle, even when the target vehicle was not following the intersection guideline.<br />
<br />
==== Statistical analysis of motion planning application results ====<br />
The data is analyzed from two perspectives, the time to recognize the in-lane target and the similarity to <br />
human driver commands. In most of cases, the proposed algorithm detects the in-line target no late than based <br />
algorithm. In addition, the proposed algorithm only recognized cases later than the base algorithm did when <br />
the surrounding target vehicles first appeared beyond the sensors’ region of interest boundaries. This means <br />
that these cases took place sufficiently beyond the safety distance, and had little influence on determining <br />
the behaviour of the subject vehicle.<br />
<br />
<center>[[Image:Figure11_YanYu.png|500px|]]</center><br />
<br />
In order to compare the similarities between the results form the proposed algorithm and human driving decisions, <br />
this article introduced another type of error, acceleration error <math>a_{x, error} = a_{x, human} - a_{x, cmd}</math>. where <math>a_{x, human}</math><br />
and <math>a_{x, cmd}</math> are the human driver’s acceleration history and the command from the proposed algorithm, <br />
respectively. The proposed algorithm showed more similar results to human drivers’ decisions than the base <br />
algorithm. <math>91.97\%</math> of the acceleration error lies in the region <math>\pm 1 m/s^2</math>. Moreover, the base algorithm <br />
possesses a limited ability to respond to different in-lane target behaviours in traffic flow. Hence, the proposed <br />
model is efficient and safe.<br />
<br />
== Conclusion ==<br />
A surrounding vehicle motion predictor based on an LSTM-RNN at multi-lane turn intersections was developed, and its application in an autonomous vehicle was evaluated. The model was trained by using the data captured on the urban road in Seoul in MPC. The evaluation results showed precise prediction accuracy and so the algorithm is safe to be applied on an autonomous vehicle. Also, the comparison with the other three base algorithms (CV/Path, V_flow/Path, and CTRV) revealed the superiority of the proposed algorithm. The evaluation results showed precise prediction accuracy. In addition, the time-to-recognize in-lane targets within the intersection improved significantly over the performance of the base algorithms. The proposed algorithm was compared with human driving data, and it showed similar longitudinal acceleration. The motion predictor can be applied to path planners when AVs travel in unconstructed environments, such as multi-lane turn intersections.<br />
<br />
== Future works ==<br />
This paper has identified several venues for future research, which include:<br />
<br />
1.Developing trajectory prediction algorithms using other machine learning algorithms, such as attention-aware neural networks.<br />
<br />
2.Applying the machine learning-based approach to infer lane change intention at motorways and main roads of urban environments.<br />
<br />
3.Extending the target road of the trajectory predictor, such as roundabouts or uncontrolled intersections, to infer yield intention.<br />
<br />
4.Learning the behavior of surrounding vehicles in real time while automated vehicles drive with real traffic.<br />
<br />
== Critiques ==<br />
The literature review is not sufficient. It should focus more on LSTM, RNN, and the study in different types of roads. Why the LSTM-RNN is used, and the background of the method is not stated clearly. There is a lack of concept so that it is difficult to distinguish between LSTM-RNN based motion predictor and motion planning.<br />
<br />
This is an interesting topic to discuss. This is a major topic for some famous vehicle companies such as Tesla, which now already has a good service called Autopilot to give self-driving and Motion Prediction. This summary can include more diagrams in architecture in the model to give readers a whole view of how the model looks like. Since it is using LSTM-RNN, include some pictures of the LSTM-RNN will be great. I think it will be interesting to discuss more applications by using this method, such as Airplane, boats.<br />
<br />
Autonomous driving is a very hot topic, and training the model with LSTM-RNN is also a meaningful topic to discuss. By the way, it would be an interesting approach to compare the performance of different algorithms or some other traditional motion planning algorithms like KF.<br />
<br />
There are some papers that discussed the accuracy of different models in vehicle predictions, such as Deep Kinematic Models for Kinematically Feasible Vehicle Trajectory Predictions[https://arxiv.org/pdf/1908.00219.pdf.] The LSTM didn't show good performance. They increased the accuracy by combing LSTM with an unconstrained model(UM) by adding an additional LSTM layer of size 128 that is used to recursively output positions instead of simultaneously outputting positions for all horizons.<br />
<br />
It may be better to provide the results of experiments to support the efficiency of LSTM-RNN, talk about the prediction of training and test sets, and compared it with other autonomous driving systems that exist in the world.<br />
<br />
The topic of surround vehicle motion prediction is analogous to the topic of autonomous vehicles. An example of an application of these frameworks would be the transportation services industry. Many companies, such as Lyft and Uber, have started testing their own commercial autonomous vehicles.<br />
<br />
It would be really helpful if some visualization or data summary can be provided to understand the content, such as the track of the car movement.<br />
<br />
The model should have been tested in other regions besides just Seoul, as driving behaviors can vary drastically from region to region.<br />
<br />
Understandably, a supervised learning problem should be evaluated on some test dataset. However, supervised learning techniques are inherently ill-suited for general planning problems. The test dataset was obtained from human driving data which is known to be extremely noisy as well as unpredictable when it comes to motion planning. It would be crucial to determine the successes of this paper based on the state-of-the-art reinforcement learning techniques.<br />
<br />
It would be better if the authors compared their method against other SOTA methods. Also one of the reasons motion planning is done using interpretable methods rather than black boxes (such as this model) is because it is hard to see where things go wrong and fix problems with the black box when they occur - this is something the authors should have also discussed.<br />
<br />
A future area of study is to combine other source of information such as signals from Lidar or car side cameras to make a better prediction model.<br />
<br />
It might be interesting and helpful to conduct some training and testing under different weather/environmental conditions, as it could provide more generalization to real-life driving scenarios. For example, foggy weather and evening (low light) conditions might affect the performance of sensors, and rainy weather might require a longer braking distance.<br />
<br />
This paper proposes an interesting, novel model prediction algorithm, using LSTM_RNN. However, since motion prediction in autonomous driving has great real-life impacts, I do believe that the evaluations of the algorithm should be more thorough. For example, more traditional motion planning algorithms such as multi-modal estimation and Kalman filters should be used as benchmarks. Moreover, the experiment results are based on Korean driving conditions only. Eastern and Western drivers can have very different driving patterns, so that should be addressed in the discussion section of the paper as well.<br />
<br />
The paper mentions that in the future, this research plans to learn the real life behaviour of automated vehicles. Seeing a possible improvement in road safety due to this research will be very interesting.<br />
<br />
This predictor is also possible to be applied in the traffic control system.<br />
<br />
== Reference ==<br />
[1] E. Choi, Crash Factors in Intersection-Related Crashes: An On-Scene Perspective (No. Dot HS 811 366), U.S. DOT Nat. Highway Traffic Safety Admin., Washington, DC, USA, 2010.<br />
<br />
[2] D. J. Phillips, T. A. Wheeler, and M. J. Kochenderfer, “Generalizable intention prediction of human drivers at intersections,” in Proc. IEEE Intell. Veh. Symp. (IV), Los Angeles, CA, USA, 2017, pp. 1665–1670.<br />
<br />
[3] B. Kim, C. M. Kang, J. Kim, S. H. Lee, C. C. Chung, and J. W. Choi, “Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network,” in Proc. IEEE 20th Int. Conf. Intell. Transp. Syst. (ITSC), Yokohama, Japan, 2017, pp. 399–404.<br />
<br />
[4] E. Strigel, D. Meissner, F. Seeliger, B. Wilking, and K. Dietmayer, “The Ko-PER intersection laserscanner and video dataset,” in Proc. 17th Int. IEEE Conf. Intell. Transp. Syst. (ITSC), Qingdao, China, 2014, pp. 1900–1901.<br />
<br />
[5] Henggang Cui, Thi Nguyen, Fang-Chieh Chou, Tsung-Han Lin, Jeff Schneider, David Bradley, Nemanja Djuric: “Deep Kinematic Models for Kinematically Feasible Vehicle Trajectory Predictions”, 2019; [http://arxiv.org/abs/1908.00219 arXiv:1908.00219].<br />
<br />
[6]Schulz, Jens & Hubmann, Constantin & Morin, Nikolai & Löchner, Julian & Burschka, Darius. (2019). Learning Interaction-Aware Probabilistic Driver Behavior Models from Urban Scenarios. 10.1109/IVS.2019.8814080.</div>Y492zhuhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Surround_Vehicle_Motion_Prediction&diff=49167Surround Vehicle Motion Prediction2020-12-05T02:33:56Z<p>Y492zhu: /* Statistical analysis of motion planning application results */</p>
<hr />
<div>DROCC: '''Surround Vehicle Motion Prediction Using LSTM-RNN for Motion Planning of Autonomous Vehicles at Multi-Lane Turn Intersections'''<br />
== Presented by == <br />
Mushi Wang, Siyuan Qiu, Yan Yu<br />
<br />
== Introduction ==<br />
<br />
This paper presents a surrounding vehicle motion prediction algorithm for multi-lane turn intersections using a Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN). More specifically, it focused on the improvement of in-lane target recognition and achieving human-like acceleration decisions at multi-lane turn intersections by introducing the learning-based target motion predictor and prediction-based motion predictor. A data-driven approach for predicting the trajectory and velocity of surrounding vehicles on urban roads at multi-lane turn intersections was described. LSTM architecture, a specific kind of RNN capable of learning long-term dependencies, is designed to manage complex vehicle motions in multi-lane turn intersections. The results show that the forecaster improves the recognition time of the leading vehicle and contributes to the improvement of prediction ability.<br />
<br />
== Previous Work ==<br />
The autonomous vehicle trajectory approaches previously used motion models like Constant Velocity and Constant Acceleration. These models are linear and are only able to handle straight motions. There are curvilinear models such as Constant Turn Rate and Velocity and Constant Turn Rate and Acceleration which handle rotations and more complex motions. Together with these models, Kalman Filter is used to predicting the vehicle trajectory. Kalman filtering is a common technique used in sensor fusion for state estimation that allows the vehicle's state to be predicted while taking into account the uncertainty associated with inputs and measurements. However, the performance of the Kalman Filter in predicting multi-step problems is not that good. Recurrent Neural Network performs significantly better than it. <br />
<br />
There are 3 main challenges to achieving fully autonomous driving on urban roads, which are scene awareness, inferring other drivers’ intentions, and predicting their future motions. Researchers are developing prediction algorithms that can simulate a driver’s intuition to improve safety when autonomous vehicles and human drivers drive together. To predict driver behavior on an urban road, there are 3 categories for the motion prediction model: (1) physics-based; (2) maneuver-based; and (3) interaction-aware. Physics-based models are simple and direct, which only consider the states of prediction vehicles kinematically. The advantage is that it has minimal computational burden among the three types. However, it is impossible to consider the interactions between vehicles. Maneuver-based models consider the driver’s intention and classified them. By predicting the driver maneuver, the future trajectory can be predicted. Identifying similar behaviors in driving is able to infer different drivers' intentions which are stated to improve the prediction accuracy. However, it still an assistant to improve physics-based models. <br />
<br />
Recurrent Neural Network (RNN) is a type of approach proposed to infer driver intention in this paper. Interaction-aware models can reflect interactions between surrounding vehicles, and predict future motions of detected vehicles simultaneously as a scene. While the prediction algorithm is more complex in computation which is often used in offline simulations. As Schulz et al. indicate, interaction models are very difficult to create as "predicting complete trajectories at once is challenging, as one needs to account for multiple hypotheses and long-term interactions between multiple agents" [6].<br />
<br />
== Motivation == <br />
Research results indicate that little research has been dedicated on predicting the trajectory of intersections. Moreover, public data sets for analyzing driver behaviour at intersections are not enough, and these data sets are not easy to collect. A model is needed to predict the various movements of the target around a multi-lane turning intersection. It is very necessary to design a motion predictor that can be used for real-time traffic.<br />
<br />
<center><br />
[[ File:intersection.png |300px]]<br />
</center><br />
<br />
== Framework == <br />
The LSTM-RNN-based motion predictor comprises three parts: (1) a data encoder; (2) an LSTM-based RNN; and (3) a data decoder depicts the architecture of the surrounding target trajectory predictor. The proposed architecture uses a perception algorithm to estimate the state of surrounding vehicles, which relies on six scanners. The output predicts the state of the surrounding vehicles and is used to determine the expected longitudinal acceleration in the actual traffic at the intersection. The following image gives a visual representation of the model.<br />
<br />
<center>[[Image:Figure1_Yan.png|800px|]]</center><br />
<br />
== LSTM-RNN based motion predictor == <br />
<br />
=== Sensor Outputs ===<br />
<br />
The input of the target perceptions is from the output of the sensors. The data collected in this article uses 6 different sensors with feature fusion to detect traffic in the range up to 100m: 1) LiDAR system outputs: Relative position, heading, velocity, and box size in local coordinates; 2) Around0View Monitoring (AVM) and 3)GPS outputs: acquire lanes, road marker, global position; 4) Gateway engine outputs: precise global position in urban road environment; 5) Micro-Autobox II and 6) a MDPS are used to control and actuate the subject. All data are stored in an industrial PC.<br />
<br />
=== Data ===<br />
Multi-lane turn intersections are the target roads in this paper. The dataset was collected using a human driven Autonomous Vehicle(AV) that was equipped with sensors to track motion the vehicle's surroundings. In addition the motion sensors they used a front camera, Around-View-Monitor and GPS to acquire the lanes, road markers and global position. The data was collected in the urban roads of Gwanak-gu, Seoul, South Korea. The training model is generated from 484 tracks collected when driving through intersections in real traffic. The previous and subsequent states of a vehicle at a particular time can be extracted. After post-processing, the collected data, a total of 16,660 data samples were generated, including 11,662 training data samples, and 4,998 evaluation data samples.<br />
<br />
=== Motion predictor ===<br />
This article proposes a data-driven method to predict the future movement of surrounding vehicles based on their previous movement, which is the sequential previous motion. The motion predictor based on the LSTM-RNN architecture in this work only uses information collected from sensors on autonomous vehicles, as shown in the figure below. The contribution of the network architecture of this study is that the future state of the target vehicle is used as the input feature for predicting the field of view. <br />
<br />
<br />
<center>[[Image:Figure7b_Yan.png|500px|]]</center><br />
<br />
<br />
==== Network architecture ==== <br />
A RNN is an artificial neural network, suitable for use with sequential data. It can also be used for time-series data, where the pattern of the data depends on the time flow. Also, it can contain feedback loops that allow activations to flow alternately in the loop.<br />
An LSTM avoids the problem of vanishing gradients by making errors flow backward without a limit on the number of virtual layers. This property prevents errors from increasing or declining over time, which can make the network train improperly. The figure below shows the various layers of the LSTM-RNN and the number of units in each layer. This structure is determined by comparing the accuracy of 72 RNNs, which consist of a combination of four input sets and 18 network configurations.<br />
<br />
<center>[[Image:Figure8_Yan.png|800px|]]</center><br />
<br />
==== Input and output features ==== <br />
In order to apply the motion predictor to the AV in motion, the speed of the data collection vehicle is added to the input sequence. The input sequence consists of relative X/Y position, relative heading angle, speed of surrounding target vehicles, and speed of data collection vehicles. The output sequence is the same as the input sequence, such as relative position, heading, and speed.<br />
<br />
==== Encoder and decoder ==== <br />
In this study, the authors introduced an encoder and decoder that process the input from the sensor and the output from the RNN, respectively. The encoder normalizes each component of the input data to rescale the data to mean 0 and standard deviation 1, while the decoder denormalizes the output data to use the same parameters as in the encoder to scale it back to the actual unit. <br />
==== Sequence length ==== <br />
The sequence length of RNN input and output is another important factor to improve prediction performance. In this study, 5, 10, 15, 20, 25, and 30 steps of 100 millisecond sampling times were compared, and 15 steps showed relatively accurate results, even among candidates The observation time is very short.<br />
<br />
== Motion planning based on surrounding vehicle motion prediction == <br />
In daily driving, experienced drivers will predict possible risks based on observations of surrounding vehicles, and ensure safety by changing behaviors before the risks occur. In order to achieve a human-like motion plan, based on the model predictive control (MPC) method, a prediction-based motion planner for autonomous vehicles is designed, which takes into account the driver’s future behavior. The cost function of the motion planner is determined as follows:<br />
\begin{equation*}<br />
\begin{split}<br />
J = & \sum_{k=1}^{N_p} (x(k|t) - x_{ref}(k|t)^T) Q(x(k|t) - x_{ref}(k|t)) +\\<br />
& R \sum_{k=0}^{N_p-1} u(k|t)^2 + R_{\Delta \mu}\sum_{k=0}^{N_p-2} (u(k+1|t) - u(k|t))^2 <br />
\end{split}<br />
\end{equation*}<br />
where <math>k</math> and <math>t</math> are the prediction step index and time index, respectively; <math>x(k|t)</math> and <math>x_{ref} (k|t)</math> are the states and reference of the MPC problem, respectively; <math>x(k|t)</math> is composed of travel distance px and longitudinal velocity vx; <math>x_{ref} (k|t)</math> consists of reference travel distance <math>p_{x,ref}</math> and reference longitudinal velocity <math>v_{x,ref}</math> ; <math>u(k|t)</math> is the control input, which is the longitudinal acceleration command; <math>N_p</math> is the prediction horizon; and Q, R, and <math>R_{\Delta \mu}</math> are the weight matrices for states, input, and input derivative, respectively, and these weight matrices were tuned to obtain control inputs from the proposed controller that were as similar as possible to those of human-driven vehicles. <br />
The constraints of the control input are defined as follows:<br />
\begin{equation*}<br />
\begin{split}<br />
&\mu_{min} \leq \mu(k|t) \leq \mu_{max} \\<br />
&||\mu(k+1|t) - \mu(k|t)|| \leq S<br />
\end{split}<br />
\end{equation*}<br />
Where <math>u_{min}</math>, <math>u_{max}</math>and S are the minimum/maximum control input and maximum slew rate of input respectively.<br />
<br />
Determine the position and speed boundary based on the predicted state:<br />
\begin{equation*}<br />
\begin{split}<br />
& p_{x,max}(k|t) = p_{x,tar}(k|t) - c_{des}(k|t) \quad p_{x,min}(k|t) = 0 \\<br />
& v_{x,max}(k|t) = min(v_{x,ret}(k|t), v_{x,limit}) \quad v_{x,min}(k|t) = 0<br />
\end{split}<br />
\end{equation*}<br />
Where <math>v_{x, limit}</math> are the speed limits of the target vehicle.<br />
<br />
== Prediction performance analysis and application to motion planning ==<br />
=== Accuracy analysis ===<br />
The proposed algorithm was compared with the results from three base algorithms, a path-following model with <br />
constant velocity, a path-following model with traffic flow and a CTRV model.<br />
<br />
We compare those algorithms according to four sorts of errors, The <math>x</math> position error <math>e_{x,T_p}</math>, <br />
<math>y</math> position error <math>e_{y,T_p}</math>, heading error <math>e_{\theta,T_p}</math>, and velocity error <math>e_{v,T_p}</math> where <math>T_p</math> denotes time <math>p</math>. These four errors are defined as follows:<br />
<br />
\begin{equation*}<br />
\begin{split}<br />
e_{x,Tp}=& p_{x,Tp} -\hat {p}_{x,Tp}\\ <br />
e_{y,Tp}=& p_{y,Tp} -\hat {p}_{y,Tp}\\ <br />
e_{\theta,Tp}=& \theta _{Tp} -\hat {\theta }_{Tp}\\ <br />
e_{v,Tp}=& v_{Tp} -\hat {v}_{Tp}<br />
\end{split}<br />
\end{equation*}<br />
<center>[[Image:Figure10.1_YanYu.png|500px|]]</center><br />
<br />
The proposed model shows significantly fewer prediction errors compare to the based algorithms in terms of mean, <br />
standard deviation(STD), and root mean square error(RMSE). Meanwhile, the proposed model exhibits a bell-shaped <br />
curve with a close to zero mean, which indicates that the proposed algorithm's prediction of human divers' <br />
intensions are relatively precise. On the other hand, <math>e_{x,T_p}</math>, <math>e_{y,T_p}</math>, <math>e_{v,T_p}</math> are bounded within <br />
reasonable levels. For instant, the three-sigma range of <math>e_{y,T_p}</math> is within the width of a lane. Therefore, <br />
the proposed algorithm can be precise and maintain safety simultaneously.<br />
<br />
=== Motion planning application ===<br />
==== Case study of a multi-lane left turn scenario ====<br />
The proposed method mimics a human driver better, by simulating a human driver's decision-making process. <br />
In a multi-lane left turn scenario, the proposed algorithm correctly predicted the trajectory of a target <br />
the vehicle, even when the target vehicle was not following the intersection guideline.<br />
<br />
==== Statistical analysis of motion planning application results ====<br />
The data is analyzed from two perspectives, the time to recognize the in-lane target and the similarity to <br />
human driver commands. In most of cases, the proposed algorithm detects the in-line target no late than based <br />
algorithm. In addition, the proposed algorithm only recognized cases later than the base algorithm did when <br />
the surrounding target vehicles first appeared beyond the sensors’ region of interest boundaries. This means <br />
that these cases took place sufficiently beyond the safety distance, and had little influence on determining <br />
the behaviour of the subject vehicle.<br />
<br />
<center>[[Image:Figure11_YanYu.png|500px|]]</center><br />
<br />
In order to compare the similarities between the results form the proposed algorithm and human driving decisions, <br />
this article introduced another type of error, acceleration error <math>a_{x, error} = a_{x, human} - a_{x, cmd}</math>. where <math>a_{x, human}</math><br />
and <math>a_{x, cmd}</math> are the human driver’s acceleration history and the command from the proposed algorithm, <br />
respectively. The proposed algorithm showed more similar results to human drivers’ decisions than the base <br />
algorithm. <math>91.97\%</math> of the acceleration error lies in the region <math>\pm 1 m/s^2</math>. Moreover, the base algorithm <br />
possesses a limited ability to respond to different in-lane target behaviours in traffic flow. Hence, the proposed <br />
model is efficient and safe.<br />
<br />
== Conclusion ==<br />
A surrounding vehicle motion predictor based on an LSTM-RNN at multi-lane turn intersections was developed, and its application in an autonomous vehicle was evaluated. The model was trained by using the data captured on the urban road in Seoul in MPC. The evaluation results showed precise prediction accuracy and so the algorithm is safe to be applied on an autonomous vehicle. Also, the comparison with the other three base algorithms (CV/Path, V_flow/Path, and CTRV) revealed the superiority of the proposed algorithm. The evaluation results showed precise prediction accuracy. In addition, the time-to-recognize in-lane targets within the intersection improved significantly over the performance of the base algorithms. The proposed algorithm was compared with human driving data, and it showed similar longitudinal acceleration. The motion predictor can be applied to path planners when AVs travel in unconstructed environments, such as multi-lane turn intersections.<br />
<br />
== Future works ==<br />
This paper has identified several venues for future research, which include:<br />
<br />
1.Developing trajectory prediction algorithms using other machine learning algorithms, such as attention-aware neural networks.<br />
<br />
2.Applying the machine learning-based approach to infer lane change intention at motorways and main roads of urban environments.<br />
<br />
3.Extending the target road of the trajectory predictor, such as roundabouts or uncontrolled intersections, to infer yield intention.<br />
<br />
4.Learning the behavior of surrounding vehicles in real time while automated vehicles drive with real traffic.<br />
<br />
== Critiques ==<br />
The literature review is not sufficient. It should focus more on LSTM, RNN, and the study in different types of roads. Why the LSTM-RNN is used, and the background of the method is not stated clearly. There is a lack of concept so that it is difficult to distinguish between LSTM-RNN based motion predictor and motion planning.<br />
<br />
This is an interesting topic to discuss. This is a major topic for some famous vehicle companies such as Tesla, which now already has a good service called Autopilot to give self-driving and Motion Prediction. This summary can include more diagrams in architecture in the model to give readers a whole view of how the model looks like. Since it is using LSTM-RNN, include some pictures of the LSTM-RNN will be great. I think it will be interesting to discuss more applications by using this method, such as Airplane, boats.<br />
<br />
Autonomous driving is a very hot topic, and training the model with LSTM-RNN is also a meaningful topic to discuss. By the way, it would be an interesting approach to compare the performance of different algorithms or some other traditional motion planning algorithms like KF.<br />
<br />
There are some papers that discussed the accuracy of different models in vehicle predictions, such as Deep Kinematic Models for Kinematically Feasible Vehicle Trajectory Predictions[https://arxiv.org/pdf/1908.00219.pdf.] The LSTM didn't show good performance. They increased the accuracy by combing LSTM with an unconstrained model(UM) by adding an additional LSTM layer of size 128 that is used to recursively output positions instead of simultaneously outputting positions for all horizons.<br />
<br />
It may be better to provide the results of experiments to support the efficiency of LSTM-RNN, talk about the prediction of training and test sets, and compared it with other autonomous driving systems that exist in the world.<br />
<br />
The topic of surround vehicle motion prediction is analogous to the topic of autonomous vehicles. An example of an application of these frameworks would be the transportation services industry. Many companies, such as Lyft and Uber, have started testing their own commercial autonomous vehicles.<br />
<br />
It would be really helpful if some visualization or data summary can be provided to understand the content, such as the track of the car movement.<br />
<br />
The model should have been tested in other regions besides just Seoul, as driving behaviors can vary drastically from region to region.<br />
<br />
Understandably, a supervised learning problem should be evaluated on some test dataset. However, supervised learning techniques are inherently ill-suited for general planning problems. The test dataset was obtained from human driving data which is known to be extremely noisy as well as unpredictable when it comes to motion planning. It would be crucial to determine the successes of this paper based on the state-of-the-art reinforcement learning techniques.<br />
<br />
It would be better if the authors compared their method against other SOTA methods. Also one of the reasons motion planning is done using interpretable methods rather than black boxes (such as this model) is because it is hard to see where things go wrong and fix problems with the black box when they occur - this is something the authors should have also discussed.<br />
<br />
A future area of study is to combine other source of information such as signals from Lidar or car side cameras to make a better prediction model.<br />
<br />
It might be interesting and helpful to conduct some training and testing under different weather/environmental conditions, as it could provide more generalization to real-life driving scenarios. For example, foggy weather and evening (low light) conditions might affect the performance of sensors, and rainy weather might require a longer braking distance.<br />
<br />
This paper proposes an interesting, novel model prediction algorithm, using LSTM_RNN. However, since motion prediction in autonomous driving has great real-life impacts, I do believe that the evaluations of the algorithm should be more thorough. For example, more traditional motion planning algorithms such as multi-modal estimation and Kalman filters should be used as benchmarks. Moreover, the experiment results are based on Korean driving conditions only. Eastern and Western drivers can have very different driving patterns, so that should be addressed in the discussion section of the paper as well.<br />
<br />
The paper mentions that in the future, this research plans to learn the real life behaviour of automated vehicles. Seeing a possible improvement in road safety due to this research will be very interesting.<br />
<br />
== Reference ==<br />
[1] E. Choi, Crash Factors in Intersection-Related Crashes: An On-Scene Perspective (No. Dot HS 811 366), U.S. DOT Nat. Highway Traffic Safety Admin., Washington, DC, USA, 2010.<br />
<br />
[2] D. J. Phillips, T. A. Wheeler, and M. J. Kochenderfer, “Generalizable intention prediction of human drivers at intersections,” in Proc. IEEE Intell. Veh. Symp. (IV), Los Angeles, CA, USA, 2017, pp. 1665–1670.<br />
<br />
[3] B. Kim, C. M. Kang, J. Kim, S. H. Lee, C. C. Chung, and J. W. Choi, “Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network,” in Proc. IEEE 20th Int. Conf. Intell. Transp. Syst. (ITSC), Yokohama, Japan, 2017, pp. 399–404.<br />
<br />
[4] E. Strigel, D. Meissner, F. Seeliger, B. Wilking, and K. Dietmayer, “The Ko-PER intersection laserscanner and video dataset,” in Proc. 17th Int. IEEE Conf. Intell. Transp. Syst. (ITSC), Qingdao, China, 2014, pp. 1900–1901.<br />
<br />
[5] Henggang Cui, Thi Nguyen, Fang-Chieh Chou, Tsung-Han Lin, Jeff Schneider, David Bradley, Nemanja Djuric: “Deep Kinematic Models for Kinematically Feasible Vehicle Trajectory Predictions”, 2019; [http://arxiv.org/abs/1908.00219 arXiv:1908.00219].<br />
<br />
[6]Schulz, Jens & Hubmann, Constantin & Morin, Nikolai & Löchner, Julian & Burschka, Darius. (2019). Learning Interaction-Aware Probabilistic Driver Behavior Models from Urban Scenarios. 10.1109/IVS.2019.8814080.</div>Y492zhuhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Mask_RCNN&diff=48442Mask RCNN2020-11-30T15:14:24Z<p>Y492zhu: /* Critiques */</p>
<hr />
<div>== Presented by == <br />
Qing Guo, Xueguang Ma, James Ni, Yuanxin Wang<br />
<br />
== Introduction == <br />
Mask RCNN [1] is a deep neural network architecture that aims to solve instance segmentation problems in computer vision. <br />
Mask R-CNN, extends Faster R-CNN [2] by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. Mask R-CNN achieved top results in all three tracks of the COCO suite of challenges [3], including instance segmentation, bounding-box object detection, and person keypoint detection. <br />
<br />
<br />
== Visual Perception tasks == <br />
<br />
Figure 1 shows a visual representation of different types of visual perception tasks:<br />
<br />
- Image Classification: Predict a set of labels to characterize the contents of an input image<br />
<br />
- Object Detection: Build on image classification but localize each object in an image<br />
<br />
- Semantic Segmentation: Associate every pixel in an input image with a class label<br />
<br />
- Instance Segmentation: Associate every pixel in an input image to a specific object<br />
<br />
[[File:instance segmentation.png | center]]<br />
<div align="center">Figure 1: Visual Perception tasks</div><br />
<br />
<br />
Mask RCNN is a deep neural network architecture for Instance Segmentation.<br />
<br />
== Related Work == <br />
Region Proposal Network: A Region Proposal Network (RPN) takes an image (of any size) as input and outputs a set of rectangular object proposals, each with an objectness score.<br />
<br />
ROI Pooling: The main use of ROI Pooling is to adjust the proposal to a uniform size. It’s better for the subsequent network to process. It maps the proposal to the corresponding position of the feature map, divide the mapped area into sections of the same size, and performs max pooling or average pooling operations on each section.<br />
<br />
Faster R-CNN: Faster R-CNN consists of two stages. The first stage, called a Region Proposal Network, proposes candidate object bounding boxes. <br />
The second stage, which is in essence Fast R-CNN, extracts features using RoIPool from each candidate box and performs classification and bounding-box regression. The features used by both stages can be shared for faster inference.<br />
<br />
[[File:FasterRCNN.png | center]]<br />
<div align="center">Figure 2: Faster RCNN architecture</div><br />
<br />
<br />
ResNet-FPN: FPN uses a top-down architecture with lateral connections to build an in-network feature pyramid from a single-scale input. FPN is actually a general architecture that can be used in conjunction with various networks, such as VGG, ResNet, etc. Faster R-CNN with an FPN backbone extracts RoI features from different levels of the feature pyramid according to their scale, but otherwise, the rest of the approach is similar to vanilla ResNet. Using a ResNet-FPN backbone for feature extraction with Mask RCNN gives excellent gains in both accuracy and speed.<br />
<br />
[[File:ResNetFPN.png | center]]<br />
<div align="center">Figure 3: ResNetFPN architecture</div><br />
<br />
== Model Architecture == <br />
The structure of mask R-CNN is quite similar to the structure of faster R-CNN. <br />
Faster R-CNN has two stages, the RPN(Region Proposal Network) first proposes candidate object bounding boxes. Then RoIPool extracts the features from these boxes. After the features are extracted, these features data can be analyzed using classification and bounding-box regression. Mask R-CNN shares the identical first stage. But the second stage is adjusted to tackle the issue of simplifying stages pipeline. Instead of only performing classification and bounding-box regression, it also outputs a binary mask for each RoI.<br />
<br />
The important concept here is that, for most recent network systems, there's a certain order to follow when performing classification <br />
and regression, because classification depends on mask predictions. Mask R-CNN, on the other hand, applies bounding-box classification and <br />
regression in parallel, which effectively simplifies the multi-stage pipeline of the original R-CNN. And just for comparison, a complete R-CNN pipeline stages involve: 1. Make region proposals; 2. Feature extraction from region proposals; 3. SVM for object classification; 4. Bounding box regression. In conclusion, stage 3 and 4 are adjusted to simplify the network procedures.<br />
<br />
The system follows the multi-task loss, which by formula equals classification loss plus bounding-box loss plus the average binary cross-entropy loss.<br />
One thing worth noticing is that for other network systems, those masks across classes compete with each other, but in this particular case, with a <br />
per-pixel sigmoid and a binary loss the masks across classes no longer compete, which makes this formula the key for good instance segmentation results.<br />
<br />
Another important concept involved is called RoIAlign. This concept is useful in stage 2 where the RoIPool extracts <br />
features from bounding-boxes. For each RoI as input, there will be a mask and a feature map as output. The mask is obtained using the FCN(Fully Convolutional Network) and the feature map is obtained using the RoIPool. The mask helps with spatial layout, which is crucial to pixel-to-pixel correspondence. The two things we desire along the procedure are: pixel-to-pixel correspondence; no quantization is performed on any coordinates involved in the RoI, its bins, or the sampling points. Pixel-to-pixel correspondence makes sure that the input and output match in size. If there is a size difference, there will be information loss, and coordinates cannot be matched. Also, instead of quantization, the coordinates are computed using bilinear interpolation to guarantee spatial correspondence.<br />
<br />
The network architecture utilized are called ResNet and ResNeXt. The depth can be either 50 or 101. ResNet-FPN(Feature Pyramid Network) is used for feature extraction. <br />
<br />
There are some implementation details that should be mentioned: first, an RoI is considered positive if it has IoU with a ground-truth box of at least 0.5 and negative otherwise. It is important because the mask loss Lmask is defined only on positive RoIs. Second, image-centric training is used to rescale images so that pixel correspondence is achieved. An example complete structure is, the proposal number is 1000 for FPN, and then run the box prediction branch on these proposals. The mask branch is then applied to the highest scoring 100 detection boxes. The mask branch can predict K masks per RoI, but only the kth mask will be used, where k is the predicted class by the classification branch. The m-by-m floating-number mask output is then resized to the RoI size and binarized at a threshold of 0.5.<br />
<br />
== Results ==<br />
[[File:ExpInstanceSeg.png | center]]<br />
<div align="center">Figure 4: Instance Segmentation Experiments</div><br />
<br />
Instance Segmentation: Based on COCO dataset, Mask R-CNN outperforms all categories comparing to MNC and FCIS which are state of art model <br />
<br />
[[File:BoundingBoxExp.png | center]]<br />
<div align="center">Figure 5: Bounding Box Detection Experiments</div><br />
<br />
Bounding Box Detection: Mask R-CNN outperforms the base variants of all previous state-of-the-art models, including the winner of the COCO 2016 Detection Challenge.<br />
<br />
<br />
== Ablation Experiments ==<br />
[[File:BackboneExp.png | center]]<br />
<div align="center">Figure 6: Backbone Architecture Experiments</div><br />
<br />
(a) Backbone Architecture: Better backbones bring expected gains: deeper networks do better, FPN outperforms C4 features, and ResNeXt improves on ResNet. <br />
<br />
[[File:MultiVSInde.png | center]]<br />
<div align="center">Figure 7: Multinomial vs. Independent Masks Experiments</div><br />
<br />
(b) Multinomial vs. Independent Masks (ResNet-50-C4): Decoupling via perclass binary masks (sigmoid) gives large gains over multinomial masks (softmax).<br />
<br />
[[File: RoIAlign.png | center]]<br />
<div align="center">Figure 8: RoIAlign Experiments 1</div><br />
<br />
(c) RoIAlign (ResNet-50-C4): Mask results with various RoI layers. Our RoIAlign layer improves AP by ∼3 points and AP75 by ∼5 points. Using proper alignment is the only factor that contributes to the large gap between RoI layers. <br />
<br />
[[File: RoIAlignExp.png | center]]<br />
<div align="center">Figure 9: RoIAlign Experiments w Experiments</div><br />
<br />
(d) RoIAlign (ResNet-50-C5, stride 32): Mask-level and box-level AP using large-stride features. Misalignments are more severe than with stride-16 features, resulting in big accuracy gaps.<br />
<br />
[[File:MaskBranchExp.png | center]]<br />
<div align="center">Figure 10: Mask Branch Experiments</div><br />
<br />
(e) Mask Branch (ResNet-50-FPN): Fully convolutional networks (FCN) vs. multi-layer perceptrons (MLP, fully-connected) for mask prediction. FCNs improve results as they take advantage of explicitly encoding spatial layout.<br />
<br />
== Conclusion ==<br />
Mask RCNN is a deep neural network aimed to solve the instance segmentation problems in machine learning or computer vision. Mask R-CNN is a conceptually simple, flexible, and general framework for object instance segmentation. It can efficiently detect objects in an image while simultaneously generating a high-quality segmentation mask for each instance. It does object detection and instance segmentation, and can also be extended to human pose estimation.<br />
It extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps.<br />
<br />
== Critiques ==<br />
In Faster RCNN, the ROI boundary is quantized. However, mask RCNN avoids quantization and used the bilinear interpolation to compute exact values of features. By solving the misalignments due to quantization, the number and location of sampling points have no impact on the result.<br />
<br />
It may be better to compare the proposed model with other NN models or even non-NN methods like spectral clustering. Also, the applications can be further discussed like geometric mesh processing and motion analysis.<br />
<br />
The paper lacks the comparisons of different methods and Mask RNN on unlabelled data, as the paper only briefly mentioned that the authors found out that Mask R_CNN can benefit from extra data, even if the data is unlabelled.<br />
<br />
The Mask RCNN has many practical applications as well. A particular example, where Mask RCNNs are applied would be in autonomous vehicles. Namely, it would be able to help with isolating pedestrians, other vehicles, lights, etc.<br />
<br />
An interesting application of Mask RCNN would be on face recognization from CCTVs. Flurry pictures of crowded people could be obtained from CCTV, so that mask RCNN can be applied to distinguish each person.<br />
<br />
The main problems for CNN architectures like Mask RCNN is the running time. Due to slow running times, Single Shot Detector algorithms are preferred for applications like video or live stream detections, where a faster running time would mean a better response to changes in frames. It would be beneficial to have a graphical representation of the Mask RCNN running times against single shot detector algorithms such as YOLOv3.<br />
<br />
It is interesting to investigate a solution of embedding instance segmentation with semantic segmentation in order to improve time performance. Because in many situations, knowing the exact boundary of an object is not necessary.<br />
<br />
== References ==<br />
[1] Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick. Mask R-CNN. arXiv:1703.06870, 2017.<br />
<br />
[2] Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, arXiv:1506.01497, 2015.<br />
<br />
[3] Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, Piotr Dollár. Microsoft COCO: Common Objects in Context. arXiv:1405.0312, 2015</div>Y492zhuhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Task_Understanding_from_Confusing_Multi-task_Data&diff=47002Task Understanding from Confusing Multi-task Data2020-11-27T08:54:21Z<p>Y492zhu: /* Critique */</p>
<hr />
<div>'''Presented By'''<br />
<br />
Qianlin Song, William Loh, Junyue Bai, Phoebe Choi<br />
<br />
= Introduction =<br />
<br />
Narrow AI is an artificial intelligence that outperforms humans in a narrowly defined task. The application of Narrow AI is becoming more and more common. For example, Narrow AI can be used for spam filtering, music recommendation services, and even self-driving cars. However, the widespread use of Narrow AI in important infrastructure functions raises some concerns. Some people think that the characteristics of Narrow AI make it fragile, and when neural networks can be used to control important systems (such as power grids, financial transactions), alternatives may be more inclined to avoid risks. While these machines help companies improve efficiency and cut costs, the limitations of Narrow AI encouraged researchers to look into General AI. <br />
<br />
General AI is a machine that can apply its learning to different contexts, which closely resembles human intelligence. This paper attempts to generalize the multi-task learning system that learns from data from multiple classification tasks. One application is image recognition. In figure 1, an image of an apple corresponds to 3 labels: “red”, “apple” and “sweet”. These labels correspond to 3 different classification tasks: color, fruit, and taste. <br />
<br />
[[File:CSLFigure1.PNG | 500px]]<br />
<br />
Currently, multi-task machines require researchers to construct a task definition. Otherwise, it will end up with different outputs with the same input value. Researchers manually assign tasks to each input in the sample to train the machine. See figure 1(a). This method incurs high annotation costs and restricts the machine’s ability to mirror the human recognition process. This paper is interested in developing an algorithm that understands task concepts and performs multi-task learning without manual task annotations. <br />
<br />
This paper proposed a new learning method called confusing supervised learning (CSL) which includes 2 functions: de-confusing function and mapping function. The first function allocates identifies an input to its respective task and the latter finds the relationship between the input and its label. See figure 1(b). To train a network of CSL, CSL-Net is constructed for representing CSL’s variables. However, this structure cannot be optimized by gradient back-propagation. This difficulty is solved by alternatively performing training for the de-confusing net and mapping net optimization. <br />
<br />
Experiments for function regression and image recognition problems were constructed and compared with multi-task learning with complete information to test CSL-Net’s performance. Experiment results show that CSL-Net can learn multiple mappings for every task simultaneously and achieve the same cognition result as the current multi-task machine sigh complete information.<br />
<br />
= Related Work =<br />
<br />
[[File:CSLFigure2.PNG | 700px]]<br />
<br />
==Multi-task learning==<br />
Multi-task learning aims to learn multiple tasks simultaneously using a shared feature representation. In multi-task learning, the task to which every sample belongs is known. By exploiting similarities and differences between tasks, the learning from one task can improve the learning of another task. (Caruana, 1997) This results in improved learning efficiency. Multi-task learning is used in disciplines like computer vision, natural language processing, and reinforcement learning. In multi-task learning, the task to which every sample belongs is known. With this task definition, the input-output mapping of every task can be represented by a unified function. However, these task definitions are manually constructed, and machines need manual task annotations to learn. Without this annotation, our goal is to understand the task concept from confusing input-label pairs. Overall, It requires manual task annotation to learn and this paper is interested in machine learning without a clear task definition and manual task annotation.<br />
<br />
==Latent variable learning==<br />
Latent variable learning aims to estimate the true function with mixed probability models. See '''figure 2a'''. In the multi-task learning problem without task annotations, samples are generated from multiple distributions instead of one distribution. While, in fact, all input-label pairs come from a unified distribution, and this distribution is estimated by a mixture of multiple probability models. Thus, Due to the lack of task information, latent variable learning is insufficient to solve the research problem, which is multi-task confusing samples.<br />
<br />
==Multi-label learning==<br />
Multi-label learning aims to assign an input to a set of classes/labels. See '''figure 2b'''. It is a generalization of multi-class classification, which classifies an input into one class. In multi-label learning, an input can be classified into more than one class. Unlike multi-task learning, multi-label does not consider the relationship between different label judgments and it is assumed that each judgment is independent. An example where multi-label learning is applicable is the scenario where a website wants to automatically assign applicable tags/categories to an article. Since an article can be related to multiple categories (eg. an article can be tagged under the politics and business categories) multi-label learning is of primary concern here.<br />
<br />
= Confusing Supervised Learning =<br />
<br />
== Description of the Problem ==<br />
<br />
Confusing supervised learning (CSL) offers a solution to the issue at hand. A major area of improvement can be seen in the choice of risk measure. In traditional supervised learning, let <math> (x,y)</math> be the training samples from <math>y=f(x)</math>, which is an identical but unknown mapping relationship. Assuming the risk measure is mean squared error (MSE), the expected risk functional is<br />
<br />
$$ R(g) = \int_x (f(x) - g(x))^2 p(x) \; \mathrm{d}x $$<br />
<br />
where <math>p(x)</math> is the data distribution of the input variable <math>x</math>. In practice, model optimizations are performed using the empirical risk<br />
<br />
$$ R_e(g) = \sum_{i=1}^n (y_i - g(x_i))^2 $$<br />
<br />
When the problem involves different tasks, the model should optimize for each data point depending on the given task. Let <math>f_j(x)</math> be the true ground-truth function for each task <math> j </math>. Therefore, for some input variable <math> x_i </math>, an ideal model <math>g</math> would predict <math> g(x_i) = f_j(x_i) </math>. With this, the risk functional can be modified to fit this new task for traditional supervised learning methods.<br />
<br />
$$ R(g) = \int_x \sum_{j=1}^n (f_j(x) - g(x))^2 p(f_j) p(x) \; \mathrm{d}x $$<br />
<br />
We call <math> (f_j(x) - g(x))^2 p(f_j) </math> the '''confusing multiple mappings'''. Then the optimal solution <math>g^*(x)</math> to the mapping is <math>\bar{f}(x) = \sum_{j=1}^n p(f_j) f_j(x)</math> under this risk functional. However, the optimal solution is not conditional on the specific task at hand but rather on the entire ground-truth functions. Therefore, for every non-trivial set of tasks where <math>f_u(x) \neq f_v(x)</math> for some input <math>x</math> and <math>u \neq v</math>, <math>R(g^*) > 0</math> which implies that there is an unavoidable confusion risk.<br />
<br />
== Learning Functions of CSL ==<br />
<br />
To overcome this issue, the authors introduce two types of learning functions:<br />
* '''Deconfusing function''' &mdash; allocation of which samples come from the same task<br />
* '''Mapping function''' &mdash; mapping relation from input to the output of every learned task<br />
<br />
Suppose there are <math>n</math> ground-truth mappings <math>\{f_j : 1 \leq j \leq n\}</math> that we wish to approximate with a set of mapping functions <math>\{g_k : 1 \leq k \leq l\}</math>. The authors define the deconfusing function as an indicator function <math>h(x, y, g_k) </math> which takes some sample <math>(x,y)</math> and determines whether the sample is assigned to task <math>g_k</math>. Under the CSL framework, the risk functional (mean squared loss) is <br />
<br />
$$ R(g,h) = \int_x \sum_{j,k} (f_j(x) - g_k(x))^2 \; h(x, f_j(x), g_k) \;p(f_j) \; p(x) \;\mathrm{d}x $$<br />
<br />
which can be estimated empirically with<br />
<br />
$$R_e(g,h) = \sum_{i=1}^m \sum_{k=1}^n |y_i - g_k(x_i)|^2 \cdot h(x_i, y_i, g_k) $$<br />
<br />
== Theoretical Results ==<br />
<br />
This novel framework yields some theoretical results to show the viability of its construction.<br />
<br />
'''Theorem 1 (Existence of Solution)'''<br />
''With the confusing supervised learning framework, there is an optimal solution''<br />
$$h^*(x, f_j(x), g_k) = \mathbb{I}[j=k]$$<br />
<br />
$$g_k^*(x) = f_k(x)$$<br />
<br />
''for each <math>k=1,..., n</math> that makes the expected risk function of the CSL problem zero.''<br />
<br />
'''Theorem 2 (Error Bound of CSL)'''<br />
''With probability at least <math>1 - \eta</math> simultaneously with finite VC dimension <math>\tau</math> of CSL learning framework, the risk measure is bounded by<br />
<br />
$$R(\alpha) \leq R_e(\alpha) + \frac{B\epsilon(m)}{2} \left(1 + \sqrt{1 + \frac{4R_e(\alpha)}{B\epsilon(m)}}\right)$$<br />
<br />
''where <math>\alpha</math> is the total parameters of learning functions <math>g, h</math>, <math>B</math> is the upper bound of one sample's risk, <math>m</math> is the size of training data and''<br />
$$\epsilon(m) = 4 \; \frac{\tau (\ln \frac{2m}{\tau} + 1) - \ln \eta / 4}{m}$$<br />
<br />
= CSL-Net =<br />
In this section, the authors describe how to implement and train a network for CSL.<br />
<br />
== The Structure of CSL-Net ==<br />
Two neural networks, deconfusing-net and mapping-net are trained to implement two learning function variables in empirical risk. The optimization target of the training algorithm is:<br />
$$\min_{g, h} R_e = \sum_{i=1}^{m}\sum_{k=1}^{n} (y_i - g_k(x_i))^2 \cdot h(x_i, y_i; g_k)$$<br />
<br />
The mapping-net is corresponding to functions set <math>g_k</math>, where <math>y_k = g_k(x)</math> represents the output of one certain task. The deconfusing-net is corresponding to function h, whose input is a sample <math>(x,y)</math> and output is an n-dimensional one-hot vector. This output vector determines which task the sample <math>(x,y)</math> should be assigned to. The core difficulty of this algorithm is that the risk function cannot be optimized by gradient back-propagation due to the constraint of one-hot output from deconfusing-net. Approximation of softmax will lead the deconfusing-net output into a non-one-hot form, which results in meaningless trivial solutions.<br />
<br />
== Iterative Deconfusing Algorithm ==<br />
To overcome the training difficulty, the authors divide the empirical risk minimization into two local optimization problems. In each single-network optimization step, the parameters of one network are updated while the parameters of another remain fixed. With one network's parameters unchanged, the problem can be solved by a gradient descent method of neural networks. <br />
<br />
'''Training of Mapping-Net''': With function h from deconfusing-net being determined, the goal is to train every mapping function <math>g_k</math> with its corresponding sample <math>(x_i^k, y_i^k)</math>. The optimization problem becomes: <math>\displaystyle \min_{g_k} L_{map}(g_k) = \sum_{i=1}^{m_k} \mid y_i^k - g_k(x_i^k)\mid^2</math>. Back-propagation algorithm can be applied to solve this optimization problem.<br />
<br />
'''Training of Deconfusing-Net''': The task allocation is re-evaluated during the training phase while the parameters of the mapping-net remain fixed. To minimize the original risk, every sample <math>(x, y)</math> will be assigned to <math>g_k</math> that is closest to label y among all different <math>k</math>s. Mapping-net thus provides a temporary solution for deconfusing-net: <math>\hat{h}(x_i, y_i) = arg \displaystyle\min_{k} \mid y_i - g_k(x_i)\mid^2</math>. The optimization becomes: <math>\displaystyle \min_{h} L_{dec}(h) = \sum_{i=1}^{m} \mid {h}(x_i, y_i) - \hat{h}(x_i, y_i)\mid^2</math>. Similarly, the optimization problem can be solved by updating the deconfusing-net with a back-propagation algorithm.<br />
<br />
The two optimization stages are carried out alternately until the solution converges.<br />
<br />
=Experiment=<br />
==Setup==<br />
<br />
3 data sets are used to compare CSL to existing methods, 1 function regression task, and 2 image classification tasks. <br />
<br />
'''Function Regression''': The function regression data comes in the form of <math>(x_i,y_i),i=1,...,m</math> pairs. However, unlike typical regression problems, there are multiple <math>f_j(x),j=1,...,n</math> mapping functions, so the goal is to recover both the mapping functions <math>f_j</math> as well as determine which mapping function corresponds to each of the <math>m</math> observations. 3 scalar-valued, scalar-input functions that intersect at several points with each other have been chosen as the different tasks. <br />
<br />
'''Colorful-MNIST''': The first image classification data set consists of the MNIST digit data that has been colored. Each observation in this modified set consists of a colored image (<math>x_i</math>) and either the color, or the digit it represents (<math>y_i</math>). The goal is to recover the classification task ("color" or "digit") for each observation and construct the 2 classifiers for both tasks. <br />
<br />
'''Kaggle Fashion Product''': This data set has more observations than the "colored-MNIST" data and consists of pictures labeled with either the “Gender”, “Category”, and “Color” of the clothing item.<br />
<br />
==Use of Pre-Trained CNN Feature Layers==<br />
<br />
In the Kaggle Fashion Product experiment, CSL trains fully-connected layers that have been attached to feature-identifying layers from pre-trained Convolutional Neural Networks.<br />
<br />
==Metrics of Confusing Supervised Learning==<br />
<br />
There are two measures of accuracy used to evaluate and compare CSL to other methods, corresponding respectively to the accuracy of the task labeling and the accuracy of the learned mapping function. <br />
<br />
'''Task Prediction Accuracy''': <math>\alpha_T(j)</math> is the average number of times the learned deconfusing function <math>h</math> agrees with the task-assignment ability of humans <math>\tilde h</math> on whether each observation in the data "is" or "is not" in task <math>j</math>.<br />
<br />
$$ \alpha_T(j) = \operatorname{max}_k\frac{1}{m}\sum_{i=1}^m I[h(x_i,y_i;f_k),\tilde h(x_i,y_i;f_j)]$$<br />
<br />
The max over <math>k</math> is taken because we need to determine which learned task corresponds to which ground-truth task.<br />
<br />
'''Label Prediction Accuracy''': <math>\alpha_L(j)</math> again chooses <math>f_k</math>, the learned mapping function that is closest to the ground-truth of task <math>j</math>, and measures its average absolute accuracy compared to the ground-truth of task <math>j</math>, <math>f_j</math>, across all <math>m</math> observations.<br />
<br />
$$ \alpha_L(j) = \operatorname{max}_k\frac{1}{m}\sum_{i=1}^m 1-\dfrac{|g_k(x_i)-f_j(x_i)|}{|f_j(x_i)|}$$<br />
<br />
==Results==<br />
<br />
Given confusing data, CSL performs better than traditional supervised learning methods, Pseudo-Label(Lee, 2013), and SMiLE(Tan et al., 2017). This is demonstrated by CSL's <math>\alpha_L</math> scores of around 95%, compared to <math>\alpha_L</math> scores of under 50% for the other methods. This supports the assertion that traditional methods only learn the means of all the ground-truth mapping functions when presented with confusing data.<br />
<br />
'''Function Regression''': In order to "correctly" partition the observations into the correct tasks, a 5-shot warm-up was used. In this situation, the CSL methods work well learning the ground-truth. That means the initialization of the neural network is set up properly.<br />
<br />
'''Image Classification''': Visualizations created through Spectral embedding confirm the task labeling proficiency of the deconfusing neural network <math>h</math>.<br />
<br />
The classification and function prediction accuracy of CSL are comparable to supervised learning programs that have been given access to the ground-truth labels.<br />
<br />
==Application of Multi-label Learning==<br />
<br />
CSL also had better accuracy than traditional supervised learning methods, Pseudo-Label(Lee, 2013), and SMiLE(Tan et al., 2017) when presented with partially labelled multi-label data <math>(x_i,y_i)</math>, where <math>y_i</math> is a <math>n</math>-long indicator vector for whether the image <math>(x_i,y_i)</math> corresponds to each of the <math>n</math> labels.<br />
<br />
Applications of multi-label classification include building a recommendation system, social media targeting, as well as detecting adverse drug reaction from text.<br />
<br />
Multi-label can be used to improve the syndrome diagnosis of a patient by focusing on multiple syndromes instead of a single syndrome.<br />
<br />
==Limitations==<br />
<br />
'''Number of Tasks''': The number of tasks is determined by increasing the task numbers progressively and testing the performance. Ideally, a better way of deciding the number of tasks is expected rather than increasing it one by one and seeing which is the minimum number of tasks that gives the smallest risk. Adding low-quality constraints to deconfusing-net is a reasonable solution to this problem.<br />
<br />
'''Learning of Basic Features''': The CSL framework is not good at learning features. So far, a pre-trained CNN backbone is needed for complicated image classification problems. Even though the effectiveness of the proposed algorithm in learning confusing data based on pre-trained features hasn't been affected, the full-connect network can only be trained based on learned CNN features. It is still a challenge for the current algorithm to learn basic features directly through a CNN structure and understand tasks simultaneously.<br />
<br />
= Conclusion =<br />
<br />
This paper proposes the CSL method for tackling the multi-task learning problem with manual task annotations in the input data. The model obtains a basic task concept by differentiating multiple mappings. The paper also demonstrates that the CSL method is an important step to moving from Narrow AI towards General AI for multi-task learning.<br />
<br />
However, there are some limitations that can be improved for future work:<br />
The repeated training process of determining the lowest best task number that has the closest to zero causes inefficiency in the learning process; The current algorithm is difficult to learn basic features directly through the CNN structure, and it is necessary to learn and train a fully connected network based on CNN features in the experiment.<br />
<br />
= Critique =<br />
<br />
The classification accuracy of CSL was made with algorithms not designed to deal with confusing data and which do not first classify the task of each observation.<br />
<br />
Human task annotation is also imperfect, so one additional application of CSL may be to attempt to flag task annotation errors made by humans, such as in sorting comments for items sold by online retailers; concerned customers, in particular, may not correctly label their comments as "refund", "order didn't arrive", "order damaged", "how good the item is" etc.<br />
<br />
This algorithm will also have a huge issue in scaling, as the proposed method requires repeated training processes, so it might be too expensive for researchers to implement and improve on this algorithm.<br />
<br />
This research paper should have included a plot on loss (of both functions) against epochs in the paper. A common issue with fixing the parameters of one network and updating the other is the variability during training. This is prevalent in other algorithms with similar training methods such as generative adversarial networks (GAN). For instance, ''mode collapse'' is the issue of one network stuck in local minima and other networks that rely on this network may receive incorrect signals during backpropagation. In the case of CSL-Net, since the Deconfusing-Net directly relies on Mapping-Net for training labels, if the Mapping-Net is unable to sufficiently converge, the Deconfusing-Net may incorrectly learn the mapping from inputs to the task. For data with high noise, oscillations may severely prolong the time needed for converge because of the strong correlation in prediction between the two networks.<br />
<br />
- It would be interesting to see this implemented in more examples, to test the robustness of different types of data.<br />
<br />
Even though this paper has already included some examples when testing the CSL in experiments, it will be better to include more detailed examples for partial-label in the "Application of Multi-label Learning" section.<br />
<br />
When using this framework for classification, the order of the one-hot classification labels for each task will likely influence the relationships learned between each task, since the same output header is used for all tasks. This may be why this method fails to learn low level representations, and requires pretraining. I would like to see more explanation in the paper about why this isn't a problem, if it was investigated.<br />
<br />
It would be a good idea to include comparison details in the summary to make results and the conclusion more convincing. For instance, though the paper introduced the result generated using confusion data, and provide some applications for multi-label learning, these two sections still fell short and could use some technical details as supporting evidence.<br />
<br />
It is interesting to investigate if the order of adding tasks will influence the model performance.<br />
<br />
= References =<br />
<br />
[1] Su, Xin, et al. "Task Understanding from Confusing Multi-task Data."<br />
<br />
[2] Caruana, R. (1997) "Multi-task learning"<br />
<br />
[3] Lee, D.-H. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. Workshop on challenges in representation learning, ICML, vol. 3, 2013, pp. 2–8. <br />
<br />
[4] Tan, Q., Yu, Y., Yu, G., and Wang, J. Semi-supervised multi-label classification using incomplete label information. Neurocomputing, vol. 260, 2017, pp. 192–202.<br />
<br />
[5] Chavdarova, Tatjana, and François Fleuret. "Sgan: An alternative training of generative adversarial networks." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9407-9415. 2018.<br />
<br />
[6] Guo-Ping Liu, Jian-Jun Yan, Yi-Qin Wang, Jing-Jing Fu, Zhao-Xia Xu, Rui Guo, Peng Qian, "Application of Multilabel Learning Using the Relevant Feature for Each Label in Chronic Gastritis Syndrome Diagnosis", Evidence-Based Complementary and Alternative Medicine, vol. 2012, Article ID 135387, 9 pages, 2012. https://doi.org/10.1155/2012/135387</div>Y492zhuhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Music_Recommender_System_Based_using_CRNN&diff=46996Music Recommender System Based using CRNN2020-11-27T08:05:44Z<p>Y492zhu: /* Critiques/ Insights: */</p>
<hr />
<div>==Introduction and Objective:==<br />
<br />
In the digital era of music streaming, companies such as Spotify and Pandora are faced with the following challenge: how to provide users with relevant and personalized music recommendations amidst the ever-growing abundance of music and user data.<br />
<br />
The objective of this paper is to implement a personalized music recommender system which takes user listening history as input and continually finds new music that captures individual user preferences.<br />
<br />
Authors of this paper argue that a music recommendation system should vary from the general recommendation system used in practice since it should combine music feature recognition and audio processing technologies to extract music features, and combine them with data on user preferences.<br />
<br />
The authors of this paper took a content-based music approach to building the recommendation system - specifically, comparing similarity of features based on audio signal.<br />
<br />
The following two-method approach to building the recommendation system was followed:<br />
#Make recommendations including genre information extracted from classification algorithms.<br />
#Make recommendations without genre information.<br />
<br />
The authors used convolutional recurrent neural networks (CRNN) and convolutional neural networks (CNN) as their main classification model.<br />
<br />
==Methods and Techniques:==<br />
<br />
The original music’s audio signal is converted into a spectrogram image. Using the image and the Short Time Fourier Transform (STFT), we convert the data into the Mel scale which is used in the CNN and CRNN models. <br />
=== Mel Scale: === <br />
Scale of pitches that are heard by listeners, which translates to equal pitch increments.<br />
<br />
[[File:Mel.png|frame|none|Mel Scale on Spectrogram]]<br />
<br />
=== Short Time Fourier Transform (STFT): ===<br />
Transformation that determines the sinusoidal frequency of the audio, with a Hanning smoothing function.<br />
=== Convolutional Neural Network (CNN): ===<br />
Neural Network that uses convolution in place of matrix multiplication for some layer calculations. By training the data, weights for inputs are updated to find the most significant data relevant to classification. These convolutional layers gather small groups of data and with kernels, and try to find patterns that can help find features in the overall data. The features are then used for classification. Padding is also used to maintain the data on the edges.<br />
<br />
[[File:Convolution.png|thumb|400px|left|Convolution Operation]]<br />
[[File:PaddingKernels.png|thumb|400px|center|Example of Padding (white 0s) and Kernels (blue square)]]<br />
<br />
=== Convolutional Recurrent Neural Network (CRNN): === <br />
Similar Neural Network as CNN, with the addition of a GRU, which is a Recurrent Neural Network (RNN). A RNN is used to treat sequential data, by reusing the activation function of previous nodes to update the output. A Gated Recurrent Unit (GRU) is used to store more long-term memory and will help train the early hidden layers.<br />
<br />
[[File:GRU441.png|thumb|400px|left|Gated Recurrent Unit (GRU)]]<br />
[[File:Recurrent441.png|thumb|400px|center|Diagram of General Recurrent Neural Network]]<br />
<br />
==Data Screening:==<br />
<br />
The authors of this paper used a publicly available music dataset made up of 25,000 30 second songs from the Free Music Archives. To ensure a balanced dataset, only 1000 songs each from the genres of classical, electronic, folk, hip-hop, instrumental, jazz and rock were used in the final model. <br />
<br />
[[File:Data441.png|thumb|200px|none|Data sorted by music genre]]<br />
<br />
==Implementation:==<br />
<br />
=== Modeling Neural Networks ===<br />
<br />
As noted previously, both CNNs and CRNNs were used to model the data. The advantage of CRNNs is that they are able to model time sequence patterns in addition to frequency features from the spectrogram, allowing for greater identification of important features. Furthermore, feature vectors produced before the classification stage could be used to improve accuracy. <br />
<br />
In implementing the neural networks, the Mel-spectrogram data was split up into training, validation, and test sets at a ratio of 8:1:1 respectively and labelled via one-hot encoding. This made it possible for the categorical data to be labelled correctly for binary classification. As opposed to classical stochastic gradient descent, the authors opted to use Adam optimization to update weights in the training phase. Binary cross-entropy was used as the loss function. <br />
<br />
In both the CNN and CRNN models, the data was trained over 100 epochs with a binary cross-entropy loss function. The sigmoid function was used as the output layer. <br />
<br />
<br />
An overview of the CNN and CRNN architecture can be found in the charts below.<br />
<br />
[[File:CNN441.png|thumb|800px|none|Implementation of CNN Model]]<br />
[[File:CRNN441.png|thumb|800px|none|Implementation of CRNN Model]]<br />
<br />
=== Music Recommendation System ===<br />
<br />
The recommendation system is computed by the cosine similarity of the extraction features from the neural network. Each genre will have a song act as a centre point for each class. The final inputs of the trained neural networks will be the feature variables. The featured variables will be used in the cosine similarity to find the best recommendations. <br />
<br />
The values are between [-1,1], where larger values are songs that have similar features. When the user inputs five songs, those songs become the new inputs in the neural networks and the features are used by the cosine similarity with other music. The largest five cosine similarities are used as the recommendations.<br />
[[File:Cosine441.png|frame|100px|none|Cosine Similarity]]<br />
<br />
== Evaluation Metrics ==<br />
=== Precision: ===<br />
* The proportion of True Positives with respect to the '''predicted''' positive cases (true positives and false positives)<br />
* For example, out of all the songs that the classifier '''predicted''' as Classical, how many are actually Classical?<br />
* Describes the rate at which the classifier predicts the true genre of songs among those predicted to be of that certain genre<br />
<br />
=== Recall: ===<br />
* The proportion of True Positives with respect to the '''actual''' positive cases (true positives and false negatives)<br />
* For example, out of all the songs that are '''actually''' Classical, how many are correctly predicted to be Classical?<br />
* Describes the rate at which the classifier predicts the true genre of songs among the correct instances of that genre<br />
<br />
=== F1-Score: ===<br />
An accuracy metric that combines the classifier’s precision and recall scores by taking the harmonic mean between the two metrics:<br />
<br />
[[File:F1441.png|frame|100px|none|F1-Score]]<br />
<br />
=== Receiver operating characteristics (ROC): ===<br />
* A graphical metric that is used to assess a classification model at different classification thresholds <br />
* In the case of a classification threshold of 0.5, this means that if <math>P(Y = k | X = x) > 0.5</math> then we classify this instance as class k<br />
* Plots the true positive rate versus false positive rate as the classification threshold is varied<br />
<br />
[[File:ROCGraph.jpg|thumb|400px|none|ROC Graph. Comparison of True Positive Rate and False Positive Rate]]<br />
<br />
=== Area Under the Curve (AUC) ===<br />
AUC is the area under the ROC in doing so, the ROC provides an aggregate measure across all possible classification thresholds.<br />
<br />
In the context of the paper: When scoring all songs as <math>Prob(Classical | X=x)</math>, it is the probability that the model ranks a random Classical song at a higher probability than a random non-Classical song.<br />
<br />
[[File:AUCGraph.jpg|thumb|400px|none|Area under the ROC curve.]]<br />
<br />
== Results ==<br />
=== Accuracy Metrics ===<br />
Looking at the accuracy metrics at the classification threshold of 0.5:<br />
<br />
[[File:TruePositiveChart.jpg|thumb|none|True Positive / False Positive Chart]]<br />
On average, CRNN outperforms CNN in true positive and false positive cases<br />
<br />
<br />
[[File:F1Chart441.jpg|thumb|400px|none|F1 Chart]]<br />
On average, CRNN outperforms CNN in F1-score <br />
<br />
<br />
[[File:AUCChart.jpg|thumb|400px|none|AUC Chart]]<br />
On average, CRNN also outperforms CNN in AUC metric<br />
<br />
<br />
CRNN models that considers the frequency features and time sequence patterns of songs have a better classification performance through metrics such as F1 score and AUC when comparing to CNN classifier.<br />
<br />
=== Evaluation of Music Recommendation System: ===<br />
<br />
* A listening experiment was performed with 30 participants to access user responses to given music recommendations.<br />
* Participants choose 5 preferred music and the recommender system gives 5 recommendations; the participants evaluated the music recommendation by recording whether the song was liked or disliked.<br />
* The recommendation system takes two approach to recommendation:<br />
** Method one uses only the value of cosine similarity<br />
** Method two uses the value of cosine similarity and information on music genre<br />
*Perform test of significance of differences in respondents to the two methods using t-statistic<br />
[[File:H0441.png|frame|100px|none|Hypothesis test between method 1 and method 2]]<br />
<br />
Comparing the two methods, <math> H_0: u_1 - u_2 = 0</math>, we have <math> t_{stat} = -4.743 < -2.037 </math> which concludes that the addition of a music genre information increases -- it is statistically significant<br />
<br />
== Conclusion: ==<br />
<br />
To increase the predictive capabilities of the music recommendation system, song genre should be a key feature.<br />
To extract the song genre from a song’s audio signals, CRNN’s are superior to CNN’s as they consider frequency in features and time sequence patterns of audio signals.<br />
<br />
== Critiques/ Insights: ==<br />
#The authors fail to give reference to the performance of current recommendation algorithms used in industry; my critique would be for the authors to bench-mark their novel approach with other recommendation algorithms such as collaborative filtering to see if there is lift in predictive capabilities.<br />
#The listening experiment used to evaluate the recommendation system only includes songs that are outputted by the model. Users may be biased if they believe all songs have come from a recommendation system. To remove bias, we suggest to have 15 songs where 5 songs are recommended and 10 songs are set. With this in the user’s mind it may remove some bias in response and give more accurate predictive capabilities.<br />
#They could go into more details about how CRNN makes it perform better than CNN, in terms of attributes of each network.<br />
# The methodology introduced in this paper is probably also suitable for movie recommendation. As music is presented as spectrograms (images) in a time sequence, and it is very similar to a movie.</div>Y492zhuhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Music_Recommender_System_Based_using_CRNN&diff=46995Music Recommender System Based using CRNN2020-11-27T08:05:26Z<p>Y492zhu: /* Critiques: */</p>
<hr />
<div>==Introduction and Objective:==<br />
<br />
In the digital era of music streaming, companies such as Spotify and Pandora are faced with the following challenge: how to provide users with relevant and personalized music recommendations amidst the ever-growing abundance of music and user data.<br />
<br />
The objective of this paper is to implement a personalized music recommender system which takes user listening history as input and continually finds new music that captures individual user preferences.<br />
<br />
Authors of this paper argue that a music recommendation system should vary from the general recommendation system used in practice since it should combine music feature recognition and audio processing technologies to extract music features, and combine them with data on user preferences.<br />
<br />
The authors of this paper took a content-based music approach to building the recommendation system - specifically, comparing similarity of features based on audio signal.<br />
<br />
The following two-method approach to building the recommendation system was followed:<br />
#Make recommendations including genre information extracted from classification algorithms.<br />
#Make recommendations without genre information.<br />
<br />
The authors used convolutional recurrent neural networks (CRNN) and convolutional neural networks (CNN) as their main classification model.<br />
<br />
==Methods and Techniques:==<br />
<br />
The original music’s audio signal is converted into a spectrogram image. Using the image and the Short Time Fourier Transform (STFT), we convert the data into the Mel scale which is used in the CNN and CRNN models. <br />
=== Mel Scale: === <br />
Scale of pitches that are heard by listeners, which translates to equal pitch increments.<br />
<br />
[[File:Mel.png|frame|none|Mel Scale on Spectrogram]]<br />
<br />
=== Short Time Fourier Transform (STFT): ===<br />
Transformation that determines the sinusoidal frequency of the audio, with a Hanning smoothing function.<br />
=== Convolutional Neural Network (CNN): ===<br />
Neural Network that uses convolution in place of matrix multiplication for some layer calculations. By training the data, weights for inputs are updated to find the most significant data relevant to classification. These convolutional layers gather small groups of data and with kernels, and try to find patterns that can help find features in the overall data. The features are then used for classification. Padding is also used to maintain the data on the edges.<br />
<br />
[[File:Convolution.png|thumb|400px|left|Convolution Operation]]<br />
[[File:PaddingKernels.png|thumb|400px|center|Example of Padding (white 0s) and Kernels (blue square)]]<br />
<br />
=== Convolutional Recurrent Neural Network (CRNN): === <br />
Similar Neural Network as CNN, with the addition of a GRU, which is a Recurrent Neural Network (RNN). A RNN is used to treat sequential data, by reusing the activation function of previous nodes to update the output. A Gated Recurrent Unit (GRU) is used to store more long-term memory and will help train the early hidden layers.<br />
<br />
[[File:GRU441.png|thumb|400px|left|Gated Recurrent Unit (GRU)]]<br />
[[File:Recurrent441.png|thumb|400px|center|Diagram of General Recurrent Neural Network]]<br />
<br />
==Data Screening:==<br />
<br />
The authors of this paper used a publicly available music dataset made up of 25,000 30 second songs from the Free Music Archives. To ensure a balanced dataset, only 1000 songs each from the genres of classical, electronic, folk, hip-hop, instrumental, jazz and rock were used in the final model. <br />
<br />
[[File:Data441.png|thumb|200px|none|Data sorted by music genre]]<br />
<br />
==Implementation:==<br />
<br />
=== Modeling Neural Networks ===<br />
<br />
As noted previously, both CNNs and CRNNs were used to model the data. The advantage of CRNNs is that they are able to model time sequence patterns in addition to frequency features from the spectrogram, allowing for greater identification of important features. Furthermore, feature vectors produced before the classification stage could be used to improve accuracy. <br />
<br />
In implementing the neural networks, the Mel-spectrogram data was split up into training, validation, and test sets at a ratio of 8:1:1 respectively and labelled via one-hot encoding. This made it possible for the categorical data to be labelled correctly for binary classification. As opposed to classical stochastic gradient descent, the authors opted to use Adam optimization to update weights in the training phase. Binary cross-entropy was used as the loss function. <br />
<br />
In both the CNN and CRNN models, the data was trained over 100 epochs with a binary cross-entropy loss function. The sigmoid function was used as the output layer. <br />
<br />
<br />
An overview of the CNN and CRNN architecture can be found in the charts below.<br />
<br />
[[File:CNN441.png|thumb|800px|none|Implementation of CNN Model]]<br />
[[File:CRNN441.png|thumb|800px|none|Implementation of CRNN Model]]<br />
<br />
=== Music Recommendation System ===<br />
<br />
The recommendation system is computed by the cosine similarity of the extraction features from the neural network. Each genre will have a song act as a centre point for each class. The final inputs of the trained neural networks will be the feature variables. The featured variables will be used in the cosine similarity to find the best recommendations. <br />
<br />
The values are between [-1,1], where larger values are songs that have similar features. When the user inputs five songs, those songs become the new inputs in the neural networks and the features are used by the cosine similarity with other music. The largest five cosine similarities are used as the recommendations.<br />
[[File:Cosine441.png|frame|100px|none|Cosine Similarity]]<br />
<br />
== Evaluation Metrics ==<br />
=== Precision: ===<br />
* The proportion of True Positives with respect to the '''predicted''' positive cases (true positives and false positives)<br />
* For example, out of all the songs that the classifier '''predicted''' as Classical, how many are actually Classical?<br />
* Describes the rate at which the classifier predicts the true genre of songs among those predicted to be of that certain genre<br />
<br />
=== Recall: ===<br />
* The proportion of True Positives with respect to the '''actual''' positive cases (true positives and false negatives)<br />
* For example, out of all the songs that are '''actually''' Classical, how many are correctly predicted to be Classical?<br />
* Describes the rate at which the classifier predicts the true genre of songs among the correct instances of that genre<br />
<br />
=== F1-Score: ===<br />
An accuracy metric that combines the classifier’s precision and recall scores by taking the harmonic mean between the two metrics:<br />
<br />
[[File:F1441.png|frame|100px|none|F1-Score]]<br />
<br />
=== Receiver operating characteristics (ROC): ===<br />
* A graphical metric that is used to assess a classification model at different classification thresholds <br />
* In the case of a classification threshold of 0.5, this means that if <math>P(Y = k | X = x) > 0.5</math> then we classify this instance as class k<br />
* Plots the true positive rate versus false positive rate as the classification threshold is varied<br />
<br />
[[File:ROCGraph.jpg|thumb|400px|none|ROC Graph. Comparison of True Positive Rate and False Positive Rate]]<br />
<br />
=== Area Under the Curve (AUC) ===<br />
AUC is the area under the ROC in doing so, the ROC provides an aggregate measure across all possible classification thresholds.<br />
<br />
In the context of the paper: When scoring all songs as <math>Prob(Classical | X=x)</math>, it is the probability that the model ranks a random Classical song at a higher probability than a random non-Classical song.<br />
<br />
[[File:AUCGraph.jpg|thumb|400px|none|Area under the ROC curve.]]<br />
<br />
== Results ==<br />
=== Accuracy Metrics ===<br />
Looking at the accuracy metrics at the classification threshold of 0.5:<br />
<br />
[[File:TruePositiveChart.jpg|thumb|none|True Positive / False Positive Chart]]<br />
On average, CRNN outperforms CNN in true positive and false positive cases<br />
<br />
<br />
[[File:F1Chart441.jpg|thumb|400px|none|F1 Chart]]<br />
On average, CRNN outperforms CNN in F1-score <br />
<br />
<br />
[[File:AUCChart.jpg|thumb|400px|none|AUC Chart]]<br />
On average, CRNN also outperforms CNN in AUC metric<br />
<br />
<br />
CRNN models that considers the frequency features and time sequence patterns of songs have a better classification performance through metrics such as F1 score and AUC when comparing to CNN classifier.<br />
<br />
=== Evaluation of Music Recommendation System: ===<br />
<br />
* A listening experiment was performed with 30 participants to access user responses to given music recommendations.<br />
* Participants choose 5 preferred music and the recommender system gives 5 recommendations; the participants evaluated the music recommendation by recording whether the song was liked or disliked.<br />
* The recommendation system takes two approach to recommendation:<br />
** Method one uses only the value of cosine similarity<br />
** Method two uses the value of cosine similarity and information on music genre<br />
*Perform test of significance of differences in respondents to the two methods using t-statistic<br />
[[File:H0441.png|frame|100px|none|Hypothesis test between method 1 and method 2]]<br />
<br />
Comparing the two methods, <math> H_0: u_1 - u_2 = 0</math>, we have <math> t_{stat} = -4.743 < -2.037 </math> which concludes that the addition of a music genre information increases -- it is statistically significant<br />
<br />
== Conclusion: ==<br />
<br />
To increase the predictive capabilities of the music recommendation system, song genre should be a key feature.<br />
To extract the song genre from a song’s audio signals, CRNN’s are superior to CNN’s as they consider frequency in features and time sequence patterns of audio signals.<br />
<br />
== Critiques/ Insights: ==<br />
#The authors fail to give reference to the performance of current recommendation algorithms used in industry; my critique would be for the authors to bench-mark their novel approach with other recommendation algorithms such as collaborative filtering to see if there is lift in predictive capabilities<br />
<br />
#The listening experiment used to evaluate the recommendation system only includes songs that are outputted by the model. Users may be biased if they believe all songs have come from a recommendation system. To remove bias, we suggest to have 15 songs where 5 songs are recommended and 10 songs are set. With this in the user’s mind it may remove some bias in response and give more accurate predictive capabilities.<br />
<br />
#They could go into more details about how CRNN makes it perform better than CNN, in terms of attributes of each network.<br />
<br />
# The methodology introduced in this paper is probably also suitable for movie recommendation. As music is presented as spectrograms (images) in a time sequence, and it is very similar to a movie.</div>Y492zhuhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Graph_Structure_of_Neural_Networks&diff=46617Graph Structure of Neural Networks2020-11-26T15:07:53Z<p>Y492zhu: /* Critique */</p>
<hr />
<div>= Presented By =<br />
<br />
Xiaolan Xu, Robin Wen, Yue Weng, Beizhen Chang<br />
<br />
= Introduction =<br />
<br />
A deep neural network is composed of neurons organized into layers and the connections between them. The architecture of a neural network can be captured by its "computational graph", where neurons are represented as nodes, and directed edges link neurons in different layers. This graphical representation demonstrates how the network transmits and transforms information through its input neurons through the hidden layer and all the way to the output neurons.<br />
<br />
In Neural Networks research, it is often important to build a relation between a neural network’s accuracy and its underlying graph structure. A natural choice is to use computational graph representation, but this has many limitations including a lack of generality and disconnection with biology/neuroscience. This disconnection between biology/neuroscience makes knowledge less transferable and interdisciplinary research more difficult.<br />
<br />
Thus, we developed a new way of representing a neural network as a graph, called a relational graph. The key insight is to focus on message exchange, rather than just on directed data flow. For example, for a fixed-width fully-connected layer, an input channel and output channel pair can be represented as a single node, while an edge in the relational graph can represent the message exchange between the two nodes. Under this formulation, using the appropriate message exchange definition, it can be shown that the relational graph can represent many types of neural network layers.<br />
<br />
WS-flex is a graph generator that allows for the systematic exploration of the design space of neural networks. Neural networks are characterized by the clustering coefficient and average path length of their relational graphs under the insights of neuroscience.<br />
<br />
= Neural Network as Relational Graph =<br />
<br />
The author proposes the concept of relational graph to study the graphical structure of neural network. Each relational graph is based on an undirected graph <math>G =(V; E)</math>, where <math>V =\{v_1,...,v_n\}</math> is the set of all the nodes, and <math>E \subseteq \{(v_i,v_j)|v_i,v_j\in V\}</math> is the set of all edges that connect nodes. Note that for the graph used here, all nodes have self edges, that is <math>(v_i,v_i)\in E</math>. <br />
<br />
To build a relational graph that captures the message exchange between neurons in the network, we associate various mathematical quantities to the graph <math>G</math>. First, a feature quantity <math>x_v</math> is associated with each node. The quantity <math>x_v</math> might be a scalar, vector or tensor depending on different types of neural networks (see the Table at the end of the section). Then a message function <math>f_{uv}(·)</math> is associated with every edge in the graph. A message function specifically takes a node’s feature as the input and then output a message. An aggregation function <math>{\rm AGG}_v(·)</math> then takes a set of messages (the outputs of message function) and outputs the updated node feature. <br />
<br />
A relation graph is a graph <math>G</math> associated with several rounds of message exchange, which transform the feature quantity <math>x_v</math> with the message function <math>f_{uv}(·)</math> and the aggregation function <math>{\rm AGG}_v(·)</math>. At each round of message exchange, each node sends messages to its neighbors and aggregates incoming messages from its neighbors. Each message is transformed at each edge through the message function, then they are aggregated at each node via the aggregation function. Suppose we have already conducted <math>r-1</math> rounds of message exchange, then the <math>r^{th}</math> round of message exchange for a node <math>v</math> can be described as<br />
<br />
<div style="text-align:center;"><math>\mathbf{x}_v^{(r+1)}= {\rm AGG}^{(r)}(\{f_v^{(r)}(\textbf{x}_u^{(r)}), \forall u\in N(v)\})</math></div> <br />
<br />
where <math>\mathbf{x}^{(r+1)}</math> is the feature of of the <math>v</math> node in the relational graph after the <math>r^{th}</math> round of update. <math>u,v</math> are nodes in Graph <math>G</math>. <math>N(u)=\{u|(u,v)\in E\}</math> is the set of all the neighbor nodes of <math>u</math> in graph <math>G</math>.<br />
<br />
To further illustrate the above, we use the basic Multilayer Perceptron (MLP) as an example. An MLP consists of layers of neurons, where each neuron performs a weighted sum over scalar inputs and outputs, followed by some non-linearity. Suppose the <math>r^{th}</math> layer of an MLP takes <math>x^{(r)}</math> as input and <math>x^{(r+1)}</math> as output, then a neuron computes <br />
<br />
<div style="text-align:center;"><math>x_i^{(r+1)}= \sigma(\Sigma_jw_{ij}^{(r)}x_j^{(r)})</math>.</div> <br />
<br />
where <math>w_{ij}^{(r)}</math> is the trainable weight and <math>\sigma</math> is the non-linearity function. Let's first consider the special case where the input and output of all the layers <math>x^{(r)}</math>, <math>1 \leq r \leq R </math> have the same feature dimensions <math>d</math>. In this scenario, we can have <math>d</math> nodes in the Graph <math>G</math> with each node representing a neuron in MLP. Each layer of neural network will correspond with a round of message exchange, so there will be <math>R</math> rounds of message exchange in total. The aggregation function here will be the summation with non-linearity transform <math>\sigma(\Sigma)</math>, while the message function is simply the scalar multipication with weight. A fully-connected, fixed-width MLP layer can then be expressed with a complete relational graph, where each node <math>x_v</math> connects to all the other nodes in <math>G</math>, that is neighborhood set <math>N(v) = V</math> for each node <math>v</math>. The figure below shows the correspondence between the complete relation graph with a 5-layer 4-dimension fully-connected MLP.<br />
<br />
<div style="text-align:center;">[[File:fully_connnected_MLP.png]]</div><br />
<br />
In fact, a fixed-width fully-connected MLP is only a special case under a much more general model family, where the message function, aggregation function, and most importantly, the relation graph structure can vary. The different relational graph will represent the different topological structure and information exchange pattern of the network, which is the property that the paper wants to examine. The plot below shows two examples of non-fully connected fixed-width MLP and their corresponding relational graphs. <br />
<br />
<div style="text-align:center;">[[File:otherMLP.png]]</div><br />
<br />
We can generalize the above definitions for fixed-width MLP to Variable-width MLP, Convolutional Neural Network (CNN), and other modern network architecture like Resnet by allowing the node feature quantity <math>\textbf{x}_j^{(r)}</math> to be a vector or tensor respectively. In this case, each node in the relational graph will represent multiple neurons in the network, and the number of neurons contained in each node at each round of message change does not need to be the same, which gives us a flexible representation of different neural network architecture. The message function will then change from the simple scalar multiplication to either matrix/tensor multiplication or convolution. The representation of these more complicated networks are described in detail in the paper, and the correspondence between different networks and their relational graph properties is summarized in the table below. <br />
<br />
<div style="text-align:center;">[[File:relational_specification.png]]</div><br />
<br />
Overall, relational graphs provide a general representation for neural networks. With proper definitions of node features and message exchange, relational graphs can represent diverse neural architectures, thereby allowing us to study the performance of different graph structures.<br />
<br />
= Exploring and Generating Relational Graphs=<br />
<br />
We will deal with the design and how to explore the space of relational graphs in this section. There are three parts we need to consider:<br />
<br />
(1) '''Graph measures''' that characterize graph structural properties:<br />
<br />
We will use one global graph measure, average path length, and one local graph measure, clustering coefficient in this paper.<br />
To explain clearly, average path length measures the average shortest path distance between any pair of nodes; the clustering coefficient measures the proportion of edges between the nodes within a given node’s neighborhood, divided by the number of edges that could possibly exist between them, averaged over all the nodes.<br />
<br />
(2) '''Graph generators''' that can generate the diverse graph:<br />
<br />
With selected graph measures, we use a graph generator to generate diverse graphs to cover a large span of graph measures. To figure out the limitation of the graph generator and find out the best, we investigate some generators including ER, WS, BA, Harary, Ring, Complete graph and results shows as below:<br />
<br />
<div style="text-align:center;">[[File:3.2 graph generator.png]]</div><br />
<br />
Thus, from the picture, we could obtain the WS-flex graph generator that can generate graphs with a wide coverage of graph measures; notably, WS-flex graphs almost encompass all the graphs generated by classic random generators mentioned above.<br />
<br />
(3) '''Computational Budget''' that we need to control so that the differences in performance of different neural networks are due to their diverse relational graph structures.<br />
<br />
It is important to ensure that all networks have approximately the same complexities so that the differences in performance are due to their relational graph structures when comparing neutral work by their diverse graph.<br />
<br />
We use FLOPS (# of multiply-adds) as the metric. We first compute the FLOPS of our baseline network instantiations (i.e. complete relational graph) and use them as the reference complexity in each experiment. From the description in section 2, a relational graph structure can be instantiated as a neural network with variable width. Therefore, we can adjust the width of a neural network to match the reference complexity without changing the relational graph structures.<br />
<br />
= Experimental Setup =<br />
The author studied the performance of 3942 sampled relational graphs (generated by WS-flex from the last section) of 64 nodes with two experiments: <br />
<br />
(1) CIFAR-10 dataset: 10 classes, 50K training images, and 10K validation images<br />
<br />
Relational Graph: all 3942 sampled relational graphs of 64 nodes<br />
<br />
Studied Network: 5-layer MLP with 512 hidden units<br />
<br />
<br />
(2) ImageNet classification: 1K image classes, 1.28M training images and 50K validation images<br />
<br />
Relational Graph: Due to high computational cost, 52 graphs are uniformly sampled from the 3942 available graphs.<br />
<br />
Studied Network: <br />
*ResNet-34, which only consists of basic blocks of 3×3 convolutions (He et al., 2016)<br />
<br />
*ResNet-34-sep, a variant where we replace all 3×3 dense convolutions in ResNet-34 with 3×3 separable convolutions (Chollet, 2017)<br />
<br />
*ResNet-50, which consists of bottleneck blocks (He et al., 2016) of 1×1, 3×3, 1×1 convolutions<br />
<br />
*EfficientNet-B0 architecture (Tan & Le, 2019)<br />
<br />
*8-layer CNN with 3×3 convolution<br />
<br />
= Discussions and Conclusions =<br />
<br />
The paper summarizes the result of the experiment among multiple different relational graphs through sampling and analyzing and list six important observations during the experiments, These are:<br />
<br />
* There are always exists graph structure that has higher predictive accuracy under Top-1 error compare to the complete graph<br />
<br />
* There is a sweet spot that the graph structure near the sweet spot usually outperform the base graph<br />
<br />
* The predictive accuracy under top-1 error can be represented by a smooth function of Average Path Length <math> (L) </math> and Clustering Coefficient <math> (C) </math><br />
<br />
* The Experiments is consistent across multiple datasets and multiple graph structure with similar Average Path Length and Clustering Coefficient.<br />
<br />
* The best graph structure can be identified easily.<br />
<br />
* There is a similarity between best artificial neurons and biological neurons.<br />
<br />
----<br />
<br />
<br />
<br />
[[File:Result2_441_2020Group16.png]]<br />
<br />
$$\text{Figure - Results from Experiments}$$<br />
<br />
== Neural networks performance depends on its structure ==<br />
During the experiment, Top-1 errors for all sampled relational graph among multiple tasks and graph structures are recorded. The parameters of the models are average path length and clustering coefficient. Heat maps were created to illustrate the difference in predictive performance among possible average path length and clustering coefficient. In '''Figure - Results from Experiments (a)(c)(f)''', The darker area represents a smaller top-1 error which indicates the model performs better than the light area.<br />
<br />
Compare with the complete graph which has parameter <math> L = 1 </math> and <math> C = 1 </math>, The best performing relational graph can outperform the complete graph baseline by 1.4% top-1 error for MLP on CIFAR-10, and 0.5% to 1.2% for models on ImageNet. Hence it is an indicator that the predictive performance of the neural networks highly depends on the graph structure, or equivalently that the completed graph does not always have the best performance. <br />
<br />
== Sweet spot where performance is significantly improved ==<br />
It had been recognized that training noises often results in inconsistent predictive results. In the paper, the 3942 graphs in the sample had been grouped into 52 bins, each bin had been colored based on the average performance of graphs that fall into the bin. By taking the average, the training noises had been significantly reduced. Based on the heat map '''Figure - Results from Experiments (f)''', the well-performing graphs tend to cluster into a special spot that the paper called “sweet spot” shown in the red rectangle, the rectangle is approximately included clustering coefficient in the range <math>[0.1,0.7]</math> and average path length within <math>[1.5,3]</math>.<br />
<br />
== Relationship between neural network’s performance and parameters == <br />
When we visualize the heat map, we can see that there is no significant jump of performance that occurred as a small change of clustering coefficient and average path length ('''Figure - Results from Experiments (a)(c)(f)'''). In addition, if one of the variables is fixed in a small range, it is observed that a second-degree polynomial is a good visualization tool for the overall trend ('''Figure - Results from Experiments (b)(d)'''). Therefore, both the clustering coefficient and average path length are highly related to neural network performance by a U-shape. <br />
<br />
== Consistency among many different tasks and datasets ==<br />
They observe that relational graphs with certain graph measures may consistently perform well regardless of how they are instantiated. The paper presents consistency uses two perspectives, one is qualitative consistency and another one is quantitative consistency.<br />
<br />
(1) '''Qualitative Consistency'''<br />
It is observed that the results are consistent from different points of view. Among multiple architecture dataset, it is observed that the clustering coefficient within <math>[0.1,0.7]</math> and average path length within <math>[1.5,3]</math> consistently outperform the baseline complete graph. <br />
<br />
(2) '''Quantitative Consistency'''<br />
Among different dataset with the network that has similar clustering coefficient and average path length, the results are correlated, The paper mentioned that ResNet-34 is much more complex than 5-layer MLP but a fixed set relational graph would perform similarly in both settings, with Pearson correlation of <math>0.658</math>, the p-value for the Null hypothesis is less than <math>10^{-8}</math>.<br />
<br />
== Top architectures can be identified efficiently ==<br />
The computation cost of finding top architectures can be significantly reduced without training the entire data set for a large value of epoch or a relatively large sample. To achieve the top architectures, the number of graphs and training epochs need to be identified. For the number of graphs, a heatmap is a great tool to demonstrate the result. In the 5-layer MLP on CIFAR-10 example, taking a sample of the data around 52 graphs would have a correlation of 0.9, which indicates that fewer samples are needed for a similar analysis in practice. When determining the number of epochs, correlation can help to show the result. In ResNet34 on ImageNet example, the correlation between the variables is already high enough for future computation within 3 epochs. This means that good relational graphs perform well even at the<br />
initial training epochs.<br />
<br />
== Well-performing neural networks have graph structure surprisingly similar to those of real biological neural networks==<br />
The way we define relational graphs and average length in the graph is similar to the way information is exchanged in network science. The biological neural network also has a similar relational graph representation and graph measure with the best-performing relational graph.<br />
<br />
While there is some organizational similarity between a computational neural network and a biological neural network, we should refrain from saying that both these networks share many similarities or are essentially the same with just different substrates. The biological neurons are still quite poorly understood and it may take a while before their mechanisms are better understood.<br />
<br />
= Critique =<br />
<br />
1. The experiment is only measuring on a single data set which might not be representative enough. As we can see in the whole paper, the "sweet spot" we talked about might be a special feature for the given data set only which is the CIFAR-10 data set. If we change the data set to another imaging data set like CK+, whether we are going to get a similar result is not shown by the paper. Hence, the result that is being concluded from the paper might not be representative enough. <br />
<br />
2. When we are fitting the model in practice, we will fit the model with more than one epoch. The order of the model fitting should be randomized since we should create more random jumps to avoid staked inside a local minimum. With the same order within each epoch, the data might be grouped by different classes or levels, the model might result in a better performance with certain classes and worse performance with other classes. In this particular example, without randomization of the training data, the conclusion might not be precise enough.<br />
<br />
3. This study shows empirical justification for choosing well-performing models from graphs differing only by average path length and clustering coefficient. An equally important question is whether there is the theoretical justification for why these graph properties may (or may not) contribute the performance of a general classifier - for example, is there a combination of these properties that is sufficient to recover the universality theorem for MLP's?<br />
<br />
4. It might be worth looking into how to identify the "sweet spot" for different datasets.<br />
<br />
5. What would be considered a "best graph structure " in the discussion and conclusion part? It seems that the intermediate result of getting an accurate result was by binning graphs into smaller bins, what should we do if the graphs can not be binned into significantly smaller bins in order to proceed with the methodologies mentioned in the paper. Both CIFAR - 10 and ImageNet seem too trivial considering the amount of variation and categories in the dataset. What would the generalizability be to other presentation of images?<br />
<br />
6. There is an interesting insight that the idea of relational graph is kind of similar to applying causal graph in neuro networks, which is also closer to biology and neuroscience because human beings learning things based on causality. This new approach may lead to higher prediction accuracy but it needs more assumption, such as correct relations and causalities.</div>Y492zhuhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:Yktan&diff=46560User:Yktan2020-11-26T04:34:35Z<p>Y492zhu: /* Critiques */</p>
<hr />
<div>== Presented by == <br />
Ruixian Chin, Yan Kai Tan, Jason Ong, Wen Cheen Chiew<br />
<br />
== Introduction ==<br />
<br />
Much of the success in training deep neural networks (DNNs) is thanks to the collection of large datasets with human annotated labels. However, human annotation is both a time-consuming and expensive task, especially for the data that requires expertise such as medical data. Furthermore, certain datasets will be noisy due to the biases introduced by different annotators.<br />
<br />
There are a few existing approaches to use datasets with noisy labels. In learning with noisy labels (LNL), most methods take a loss correction approach. An example of a popular loss correction approach is the bootstrapping loss approach. Another approach to reduce annotation cost is semi-supervised learning (SSL), where the training data consists of labeled and unlabeled samples.<br />
<br />
This paper introduces DivideMix, which combines approaches from LNL and SSL. One unique thing about DivideMix is that it discards sample labels that are highly likely to be noisy and leverages these noisy samples as unlabeled data instead. This prevents the model from overfitting and improves generalization performance. Key contributions of this work are:<br />
1) Co-divide, which trains two networks simultaneously, aims to improve generalization and avoiding confirmation bias.<br />
2) During SSL phase, an improvement is made on an existing method (MixMatch) by combining it with another method (MixUp).<br />
3) Significant improvements to state-of-the-art results on multiple conditions are experimentally shown while using DivideMix. Extensive ablation study and qualitative results are also shown to examine the effect of different components.<br />
<br />
== Motivation ==<br />
<br />
While much has been achieved in training DNNs with noisy labels and SSL methods individually, not much progress has been made in exploring their underlying connections and building on top of the two approaches simultaneously. <br />
<br />
Existing LNL methods aim to correct the loss function by:<br />
<ol><br />
<li> Treating all samples equally and correcting loss explicitly or implicitly through relabeling of the noisy samples<br />
<li> Reweighting training samples or separating clean and noisy samples, which results in correction of the loss function<br />
</ol><br />
<br />
A few examples of LNL methods include:<br />
<ol><br />
<li> Estimating the noise transition matrix to correct the loss function<br />
<li> Leveraging DNNs to correct labels and using them to modify the loss<br />
<li> Reweighting samples so that noisy labels contribute less to the loss<br />
</ol><br />
<br />
However, these methods each have some downsides. For example, it is very challenging to correctly estimate the noise transition matrix in the first method; for the second method, DNNs tend to overfit to datasets with high noise ratio; for the third method, we need to be able to identify clean samples, which has also proven to be challenging.<br />
<br />
On the other hand, SSL methods mostly leverage unlabeled data using regularization to improve model performance. A recently proposed method, MixMatch incorporates the two classes of regularization – consistency regularization which enforces the model to produce consistent predictions on augmented input data, and entropy minimization which encourages the model to give high-confidence predictions on unlabeled data, as well as MixUp regularization. <br />
<br />
DivideMix partially adopts LNL in that it removes the labels that are highly likely to be noisy by using co-divide to avoid the confirmation bias problem. It then utilizes the noisy samples as unlabeled data and adopts an improved version of MixMatch (SSL) which accounts for the label noise during the label co-refinement and co-guessing phase. By incorporating SSL techniques into LNL and taking the best of both worlds, DivideMix aims to produce highly promising results in training DNNs by better addressing the confirmation bias problem, more accurately distinguishing and utilizing noisy samples, and performing well under high levels of noise.<br />
<br />
== Model Architecture ==<br />
<br />
DivideMix leverages semi-supervised learning to achieve effective modeling. The sample is first split into a labeled set and an unlabeled set. This is achieved by fitting a Gaussian Mixture Model as a per-sample loss distribution. The unlabeled set is made up of data points with discarded labels deemed noisy. Then, to avoid confirmation bias, which is typical when a model is self-training, two models are being trained simultaneously to filter error for each other. This is done by dividing the data using one model and then training the other model. This algorithm, known as Co-divide, keeps the two networks from converging when training, which avoids the bias from occurring. Figure 1 describes the algorithm in graphical form.<br />
<br />
[[File:ModelArchitecture.PNG | center]]<br />
<br />
<div align="center">Figure 1: Model Architecture of DivideMix</div><br />
<br />
For each epoch, the network divides the dataset into a labeled set consisting of clean data, and an unlabeled set consisting of noisy data, which is then used as training data for the other network, where training is done in mini-batches. For each batch of the labelled samples, co-refinement is performed by using the ground truth label <math> y_b </math>, the predicted label <math> p_b </math>, and the posterior is used as the weight, <math> w_b </math>. <br />
<br />
<center><math> \bar{y}_b = w_b y_b + (1-w_b) p_b </math></center> <br />
<br />
Then, a sharpening function is implemented on this weighted sum to produce the estimate, <math> \hat{y}_b </math>. Using all these predicted labels, the unlabeled samples will then be assigned a "co-guessed" label, which should produce a more accurate prediction. Having calculated all these labels, MixMatch is applied to the combined mini-batch of labeled, <math> \hat{X} </math> and unlabeled data, <math> \hat{U} </math>, where, for a pair of samples and their labels, one new sample and new label is produced. More specifically, for a pair of samples <math> (x_1,x_2) </math> and their labels <math> (p_1,p_2) </math>, the mixed sample <math> (x',p') </math> is:<br />
<br />
<center><br />
<math><br />
\begin{alignat}{2}<br />
<br />
\lambda &\sim Beta(\alpha, \alpha) \\<br />
\lambda ' &= max(\lambda, 1 - \lambda) \\<br />
x' &= \lambda ' x_1 + (1 - \lambda ' ) x_2 \\<br />
p' &= \lambda ' p_1 + (1 - \lambda ' ) p_2 \\<br />
<br />
\end{alignat}<br />
</math><br />
</center> <br />
<br />
MixMatch transforms <math> \hat{X} </math> and <math> \hat{U} </math> into <math> X' </math> and <math> U' </math>. Then, the loss on <math> X' </math>, <math> L_X </math> (Cross-entropy loss) and the loss on <math> U' </math>, <math> L_U </math> (Mean Squared Error) are calculated. A regularization term, <math> L_{reg} </math>, is introduced to regularize the model's average output across all samples in the mini-batch. Then, the total loss is calculated as:<br />
<br />
<center><math> L = L_X + \lambda_u L_U + \lambda_r L_{reg} </math></center> ,<br />
<br />
where <math> \lambda_r </math> is set to 1, and <math> \lambda_u </math> is used to control the unsupervised loss.<br />
<br />
Lastly, the stochastic gradient descent formula is updated with the calculated loss, <math> L </math>, and the estimated parameters, <math> \boldsymbol{ \theta } </math>.<br />
<br />
== Results ==<br />
'''Applications'''<br />
<br />
The method was validated using four benchmark datasets: CIFAR-10, CIFAR100 (Krizhevsky & Hinton, 2009)(both contain 50K training images and 10K test images of size 32 × 32), Clothing1M (Xiao et al., 2015), and WebVision (Li et al., 2017a).<br />
Two types of label noise are used in the experiments: symmetric and asymmetric.<br />
An 18-layer PreAct Resnet (He et al., 2016) is trained using SGD with a momentum of 0.9, a weight decay of 0.0005, and a batch size of 128. The network is trained for 300 epochs. The initial learning rate was set to 0.02, and reduced by a factor of 10 after 150 epochs. Before applying the Co-divide and MixMatch strategies, the models were first independently trained over the entire dataset using cross-entropy loss during a "warm-up" period. Initially, training the models in this way prepares a more regular distribution of losses to improve upon in subsequent epochs. The warm-up period is 10 epochs for CIFAR-10 and 30 epochs for CIFAR-100. For all CIFAR experiments, we use the same hyperparameters M = 2, T = 0.5, and α = 4. τ is set as 0.5 except for 90% noise ratio when it is set as 0.6.<br />
<br />
<br />
'''Comparison of State-of-the-Art Methods'''<br />
<br />
The effectiveness of DivideMix was shown by comparing the test accuracy with the most recent state-of-the-art methods: <br />
Meta-Learning (Li et al., 2019) proposes a gradient-based method to find model parameters that are more noise-tolerant; <br />
Joint-Optim (Tanaka et al., 2018) and P-correction (Yi & Wu, 2019) jointly optimize the sample labels and the network parameters;<br />
M-correction (Arazo et al., 2019) models sample loss with BMM and apply MixUp.<br />
The following are the results on CIFAR-10 and CIFAR-100 with different levels of symmetric label noise ranging from 20% to 90%. Both the best test accuracy across all epochs and the averaged test accuracy over the last 10 epochs were recorded in the following table:<br />
<br />
<br />
[[File:divideMixtable1.PNG | center]]<br />
<br />
From table1, the author noticed that none of these methods can consistently outperform others across different datasets. M-correction excels at symmetric noise, whereas Meta-Learning performs better for asymmetric noise. DivideMix outperforms state-of-the-art methods by a large margin across all noise ratios. The improvement is substantial (∼10% of accuracy) for the more challenging CIFAR-100 with high noise ratios.<br />
<br />
DivideMix was compared with the state-of-the-art methods with the other two datasets: Clothing1M and WebVision. It also shows that DivideMix consistently outperforms state-of-the-art methods across all datasets with different types of label noise. For WebVision, DivideMix achieves more than 12% improvement in top-1 accuracy. <br />
<br />
<br />
'''Ablation Study'''<br />
<br />
The effect of removing different components to provide insights into what makes DivideMix successful. We analyze the results in Table 5 as follows.<br />
<br />
<br />
[[File:DivideMixtable5.PNG | center]]<br />
<br />
The authors find that both label refinement and input augmentation are beneficial for DivideMix.<br />
<br />
== Conclusion ==<br />
<br />
This paper provides a new and effective algorithm for learning with noisy labels by leveraging SSL. The DivideMix method trains two networks simultaneously and utilizes co-guessing and co-labeling effectively, therefore it is a robust approach to dealing with noise in datasets. DivideMix has also been tested using various datasets with the results consistently being one of the best when compared to other advanced methods.<br />
<br />
Future work of DivideMix is to create an adaptation for other applications such as Natural Language Processing, and incorporating the ideas of SSL and LNL into DivideMix architecture.<br />
<br />
== Critiques/ Insights ==<br />
<br />
1. While combining both models makes the result better, the author did not show the relative time increase using this new combined methodology, which is very crucial considering training a large amount of data, especially for images. In addition, it seems that the author did not perform much on hyperparameters tuning for the combined model.<br />
<br />
2. There is an interesting insight, which is when noise ratio increases from 80% to 90%, the accuracy of DivideMix drops dramatically in both datasets.<br />
<br />
== References ==<br />
Eric Arazo, Diego Ortego, Paul Albert, Noel E. O’Connor, and Kevin McGuinness. Unsupervised<br />
label noise modeling and loss correction. In ICML, pp. 312–321, 2019.<br />
<br />
David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, and Colin<br />
Raffel. Mixmatch: A holistic approach to semi-supervised learning. NeurIPS, 2019.<br />
<br />
Yifan Ding, Liqiang Wang, Deliang Fan, and Boqing Gong. A semi-supervised two-stage approach<br />
to learning from noisy labels. In WACV, pp. 1215–1224, 2018.</div>Y492zhuhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Describtion_of_Text_Mining&diff=46532Describtion of Text Mining2020-11-26T03:59:41Z<p>Y492zhu: /* Conclusion */</p>
<hr />
<div>== Presented by == <br />
Yawen Wang, Danmeng Cui, Zijie Jiang, Mingkang Jiang, Haotian Ren, Haris Bin Zahid<br />
<br />
== Introduction ==<br />
This paper focuses on the different text mining tasks and the existence of text mining in healthcare and biomedical domains. The text mining field has been popular as a result of the amount of text data that is available in different forms. The text data is bound to grow even more in 2020, indicating a 50 times growth since 2010. Text is a kind of unstructured information, which is easy for humans to construct and understand, but it is difficult for machines. Hence, there is a need to design algorithms to effectively process this avalanche of text. To further explore the text mining field, the related text mining approaches can be considered. The different text mining approaches relate to two main methods: knowledge delivery and traditional data mining methods. <br />
<br />
The authors note that knowledge delivery methods involve the application of different steps to a specific data set to create specific patterns. Research in knowledge delivery methods has evolved over the years due to advances in hardware and software technology. On the other hand, data mining has experienced substantial development through the intersection of three fields: databases, machine learning, and statistics. As brought out by the authors, text mining approaches focus on the exploration of information from a specific text. The information explored is in the form of structured, semi-structured, and unstructured text. It is important to note that text mining covers different sets of algorithms and topics that include information retrieval. The topics and algorithms are used for analyzing different text forms.<br />
<br />
== Text Representation and Encoding ==<br />
In this section of the paper, the authors explore the different ways in which the text can be represented on a large collection of documents. One common way of representing the documents is in the form of a bag of words. The bag of words considers the occurrences of different terms. In different text mining applications, documents are ranked and represented as vectors so as to display the significance of any word. The authors note that the three basic models used are vector space, inference network, and the probabilistic models. The vector space model is used to represent documents by converting them into vectors. In the model, a variable is used to represent each model to indicate the importance of the word in the document. The words are weighted using the TF-IDF scheme computed as <br />
<br />
$$<br />
q(w)=f_d(w)*log{\frac{|D|}{f_D(w)}}<br />
$$<br />
<br />
In many text mining algorithms, one of the key components is preprocessing. Preprocessing consists of different tasks that include filtering, tokenization, stemming, and lemmatization. The first step is tokenization, where a character sequence is broken down into different words or phrases. After the breakdown, filtering is carried out to remove some words. The various word inflected forms are grouped together through lemmatization, and later, the derived roots of the derived words are obtained through stemming.<br />
<br />
== Classification ==<br />
Classification in Text Mining aims to assigned predefined classes to text documents. For a set <math>\mathcal{D} = {d_1, d_2, ... d_n}</math> of documents, such that each <math>d_i</math> is mapped to a label <math>l_i</math> from the set <math>\mathcal{L} = {l_1, l_2, ... l_k}</math>. The goal is to find a classification model <math>f</math> such that: <math>\\</math><br />
$$<br />
f: \mathcal{D} \rightarrow \mathcal{L} \quad \quad \quad f(\mathcal{d}) = \mathcal{l}<br />
$$<br />
The author illustrates 4 different classifiers that are commonly used in text mining.<br />
<br />
<br />
'''1. Naive Bayes Classifier''' <br />
<br />
Bayes rule is used to classify new examples and select the class that is most has the generated result. <br />
Naive Bayes Classifier models the distribution of documents in each class using a probabilistic model assuming that the distribution<br />
of different terms is independent of each other. The models commonly used in this classifier tried to find the posterior probability of a class based on the distribution and assumes that the documents generated are based on a mixture model parameterized by <math>\theta</math> and compute the likelihood of a document using the sum of probabilities over all mixture component.<br />
<br />
'''2. Nearest Neighbour Classifier'''<br />
<br />
Nearest Neighbour Classifier uses distance-based measures to perform the classification. The documents which belong to the same class are more likely "similar" or close to each other based on the similarity measure. The classification of the test documents is inferred from the class labels of similar documents in the training set.<br />
<br />
'''3. Decision Tree Classifier'''<br />
<br />
A hierarchical tree of the training instances, in which a condition on the attribute value is used to divide the data hierarchically. The decision tree recursively partitions the training data set into smaller subdivisions based on a set of tests defined at each node or branch. Each node of the tree is a test of some attribute of the training instance, and each branch descending from the node corresponds to one of the values of this attribute. The conditions on the nodes are commonly defined by the terms in the text documents.<br />
<br />
'''4. Support Vector Machines'''<br />
<br />
SVM is a form of Linear Classifiers which are models that makes a classification decision based on the value of the linear combinations of the documents features. The output of a linear predictor is defined to the <math> y=\vec{a} \cdot \vec{x} + b</math> where <math>\vec{x}</math> is the normalized document word frequency vector, <math>\vec{a}</math> is a vector of coefficient and <math>b</math> is a scalar. Support Vector Machines attempts to find a linear separators between various classes. An advantage of the SVM method is it is robust to high dimensionality.<br />
<br />
== Clustering ==<br />
Clustering has been extensively studied in the context of the text as it has a wide range of applications such as visualization and document organization.<br />
<br />
== Information Extraction ==<br />
Information Extraction (IE) is the process of extracting useful, structured information from unstructured or semi-structured text. It automatically extracts based on our command. <br />
<br />
For example, consider the following sentence, “XYZ company was founded by Peter in the year of 1950”<br />
We can identify the following information:<br />
<br />
Founderof(Peter, XYZ)<br />
Foundedin(1950, XYZ)<br />
<br />
The author mentioned 4 parts that are important for Information Extraction<br />
<br />
'''1. Namely Entity Recognition(NER)'''<br />
This is the process of identifying real-world entity from free text, such as "Apple Inc.", "Donald Trump", "PlayStation 5" etc. Moreover, the task is to identify the category of these entities, such as "Apple Inc." is in the category of the company, "Donald Trump" is in the category of the USA president, and "PlayStation 5" is in the category of the entertainment system. <br />
<br />
'''2. Hidden Markov Model'''<br />
Since traditional probabilistic classification does not consider the predicted labels of neighbor words, we use the Hidden Markov Model when doing Information Extraction. This model is different because it considers the label of one word depends on the previous words that appeared. <br />
<br />
'''3. Conditional Random Fields'''<br />
This is a technique that is widely used in Information Extraction. The definition of it is related to graph theory. <br />
let G = (V, E) be a graph and Yv stands for the index of the vertices in G. Then (X, Y) is a conditional random field, when the random variables Yv, conditioned on X, obey Markov property with respect to the graph, and:<br />
p(Yv |X, Yw ,w , v) = p(Yv |X, Yw ,w ∼ v), where w ∼ v means w and v are neighbors in G.<br />
<br />
'''4. Relation Extraction'''<br />
This is a task of finding semantic relationships between word entities in text documents. Such as "Seth Curry" is the brother of "Stephen Curry", if there is a document including these two names, the task is to identify the relationship of these two entities.<br />
<br />
== Biomedical Application ==<br />
<br />
Text mining has several applications in the domain of biomedical sciences. The explosion of academic literature in the field has made it quite hard for scientists to keep up with novel research. This is why text mining techniques are ever so important in making the knowledge digestible.<br />
<br />
The text mining techniques are able to extract meaningful information from large data by making use of biomedical ontology, which is a compilation of a common set of terms used in an area of knowledge. The Unified Medical Language System (UMLS) is the most comprehensive such resource, consisting of definitions of biomedical jargon. Several information extraction algorithms rely on the ontology to perform tasks such as Named Entity Recognition (NER) and Relation Extraction.<br />
<br />
NER involves locating and classifying biomedical entities into meaningful categories and assigning semantic representation to those entities. The NER methods can be broadly grouped into Dictionary-based, Rule-based and Statistical approaches. Relation extraction, on the other hand, is the process of determining relationships between the entities. This is accomplished mainly by identifying the correlation between entities through analyzing the frequency of terms, as well as rules defined by domain experts. Moreover, modern algorithms are also able to summarize large documents and answer natural language questions posed by humans.<br />
<br />
== Conclusion ==<br />
<br />
This paper gave a holistic overview of the methods and applications of text mining, particularly its relevance in the biomedical domain. It highlights several popular algorithms and summarizes them along with their advantages, limitations and some potential situations where they could be used. Because of ever-growing data, for example, the very high volume of scientific literature being produced every year, the interest in this field is massive and is bound to grow in the future.<br />
<br />
== References ==<br />
<br />
Allahyari, M., Pouriyeh, S., Assefi, M., Safaei, S., Trippe, E. D., Gutierrez, J. B., & Kochut, K. (2017). A brief survey of text mining: Classification, clustering, and extraction techniques. arXiv preprint arXiv:1707.02919.</div>Y492zhuhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Describtion_of_Text_Mining&diff=46527Describtion of Text Mining2020-11-26T03:40:00Z<p>Y492zhu: /* Classification */</p>
<hr />
<div>== Presented by == <br />
Yawen Wang, Danmeng Cui, Zijie Jiang, Mingkang Jiang, Haotian Ren, Haris Bin Zahid<br />
<br />
== Introduction ==<br />
This paper focuses on the different text mining tasks and the existence of text mining in healthcare and biomedical domains. The text mining field has been popular as a result of the amount of text data that is available in different forms. The text data is bound to grow even more in 2020, indicating a 50 times growth since 2010. Text is a kind of unstructured information, which is easy for humans to construct and understand, but it is difficult for machines. Hence, there is a need to design algorithms to effectively process this avalanche of text. To further explore the text mining field, the related text mining approaches can be considered. The different text mining approaches relate to two main methods: knowledge delivery and traditional data mining methods. <br />
<br />
The authors note that knowledge delivery methods involve the application of different steps to a specific data set to create specific patterns. Research in knowledge delivery methods has evolved over the years due to advances in hardware and software technology. On the other hand, data mining has experienced substantial development through the intersection of three fields: databases, machine learning, and statistics. As brought out by the authors, text mining approaches focus on the exploration of information from a specific text. The information explored is in the form of structured, semi-structured, and unstructured text. It is important to note that text mining covers different sets of algorithms and topics that include information retrieval. The topics and algorithms are used for analyzing different text forms.<br />
<br />
== Text Representation and Encoding ==<br />
In this section of the paper, the authors explore the different ways in which the text can be represented on a large collection of documents. One common way of representing the documents is in the form of a bag of words. The bag of words considers the occurrences of different terms. In different text mining applications, documents are ranked and represented as vectors so as to display the significance of any word. The authors note that the three basic models used are vector space, inference network, and the probabilistic models. The vector space model is used to represent documents by converting them into vectors. In the model, a variable is used to represent each model to indicate the importance of the word in the document. The words are weighted using the TF-IDF scheme computed as <br />
<br />
$$<br />
q(w)=f_d(w)*log{\frac{|D|}{f_D(w)}}<br />
$$<br />
<br />
In many text mining algorithms, one of the key components is preprocessing. Preprocessing consists of different tasks that include filtering, tokenization, stemming, and lemmatization. The first step is tokenization, where a character sequence is broken down into different words or phrases. After the breakdown, filtering is carried out to remove some words. The various word inflected forms are grouped together through lemmatization, and later, the derived roots of the derived words are obtained through stemming.<br />
<br />
== Classification ==<br />
Classification in Text Mining aims to assigned predefined classes to text documents. For a set <math>\mathcal{D} = {d_1, d_2, ... d_n}</math> of documents, such that each <math>d_i</math> is mapped to a label <math>l_i</math> from the set <math>\mathcal{L} = {l_1, l_2, ... l_k}</math>. The goal is to find a classification model <math>f</math> such that: <math>\\</math><br />
$$<br />
f: \mathcal{D} \rightarrow \mathcal{L} \quad \quad \quad f(\mathcal{d}) = \mathcal{l}<br />
$$<br />
The author illustrates 4 different classifiers that are commonly used in text mining.<br />
<br />
<br />
'''1. Naive Bayes Classifier''' <br />
<br />
Bayes rule is used to classify new examples and select the class that is most has the generated result. <br />
Naive Bayes Classifier models the distribution of documents in each class using a probabilistic model assuming that the distribution<br />
of different terms is independent of each other. The models commonly used in this classifier tried to find the posterior probability of a class based on the distribution and assumes that the documents generated are based on a mixture model parameterized by <math>\theta</math> and compute the likelihood of a document using the sum of probabilities over all mixture component.<br />
<br />
'''2. Nearest Neighbour Classifier'''<br />
<br />
Nearest Neighbour Classifier uses distance-based measures to perform the classification. The documents which belong to the same class are more likely "similar" or close to each other based on the similarity measure. The classification of the test documents is inferred from the class labels of similar documents in the training set.<br />
<br />
'''3. Decision Tree Classifier'''<br />
<br />
A hierarchical tree of the training instances, in which a condition on the attribute value is used to divide the data hierarchically. The decision tree recursively partitions the training data set into smaller subdivisions based on a set of tests defined at each node or branch. Each node of the tree is a test of some attribute of the training instance, and each branch descending from the node corresponds to one of the values of this attribute. The conditions on the nodes are commonly defined by the terms in the text documents.<br />
<br />
'''4. Support Vector Machines'''<br />
<br />
SVM is a form of Linear Classifiers which are models that makes a classification decision based on the value of the linear combinations of the documents features. The output of a linear predictor is defined to the <math> y=\vec{a} \cdot \vec{x} + b</math> where <math>\vec{x}</math> is the normalized document word frequency vector, <math>\vec{a}</math> is a vector of coefficient and <math>b</math> is a scalar. Support Vector Machines attempts to find a linear separators between various classes. An advantage of the SVM method is it is robust to high dimensionality.<br />
<br />
== Clustering ==<br />
Clustering has been extensively studied in the context of the text as it has a wide range of applications such as visualization and document organization.<br />
<br />
== Information Extraction ==<br />
Information Extraction (IE) is the process of extracting useful, structured information from unstructured or semi-structured text. It automatically extracts based on our command. <br />
<br />
For example, consider the following sentence, “XYZ company was founded by Peter in the year of 1950”<br />
We can identify the following information:<br />
<br />
Founderof(Peter, XYZ)<br />
Foundedin(1950, XYZ)<br />
<br />
The author mentioned 4 parts that are important for Information Extraction<br />
<br />
'''1. Namely Entity Recognition(NER)'''<br />
This is the process of identifying real-world entity from free text, such as "Apple Inc.", "Donald Trump", "PlayStation 5" etc. Moreover, the task is to identify the category of these entities, such as "Apple Inc." is in the category of the company, "Donald Trump" is in the category of the USA president, and "PlayStation 5" is in the category of the entertainment system. <br />
<br />
'''2. Hidden Markov Model'''<br />
Since traditional probabilistic classification does not consider the predicted labels of neighbor words, we use the Hidden Markov Model when doing Information Extraction. This model is different because it considers the label of one word depends on the previous words that appeared. <br />
<br />
'''3. Conditional Random Fields'''<br />
This is a technique that is widely used in Information Extraction. The definition of it is related to graph theory. <br />
let G = (V, E) be a graph and Yv stands for the index of the vertices in G. Then (X, Y) is a conditional random field, when the random variables Yv, conditioned on X, obey Markov property with respect to the graph, and:<br />
p(Yv |X, Yw ,w , v) = p(Yv |X, Yw ,w ∼ v), where w ∼ v means w and v are neighbors in G.<br />
<br />
'''4. Relation Extraction'''<br />
This is a task of finding semantic relationships between word entities in text documents. Such as "Seth Curry" is the brother of "Stephen Curry", if there is a document including these two names, the task is to identify the relationship of these two entities.<br />
<br />
== Biomedical Application ==<br />
<br />
Text mining has several applications in the domain of biomedical sciences. The explosion of academic literature in the field has made it quite hard for scientists to keep up with novel research. This is why text mining techniques are ever so important in making the knowledge digestible.<br />
<br />
The text mining techniques are able to extract meaningful information from large data by making use of biomedical ontology, which is a compilation of a common set of terms used in an area of knowledge. The Unified Medical Language System (UMLS) is the most comprehensive such resource, consisting of definitions of biomedical jargon. Several information extraction algorithms rely on the ontology to perform tasks such as Named Entity Recognition (NER) and Relation Extraction.<br />
<br />
NER involves locating and classifying biomedical entities into meaningful categories and assigning semantic representation to those entities. The NER methods can be broadly grouped into Dictionary-based, Rule-based and Statistical approaches. Relation extraction, on the other hand, is the process of determining relationships between the entities. This is accomplished mainly by identifying the correlation between entities through analyzing the frequency of terms, as well as rules defined by domain experts. Moreover, modern algorithms are also able to summarize large documents and answer natural language questions posed by humans.<br />
<br />
== Conclusion ==<br />
<br />
This paper gave a holistic overview of the methods and applications of text mining, particularly its relevance in the biomedical domain. It highlights several popular algorithms along with their limitations. Because of ever-growing data, the interest in this field is massive and is bound to grow in the future.<br />
<br />
== References ==<br />
<br />
Allahyari, M., Pouriyeh, S., Assefi, M., Safaei, S., Trippe, E. D., Gutierrez, J. B., & Kochut, K. (2017). A brief survey of text mining: Classification, clustering, and extraction techniques. arXiv preprint arXiv:1707.02919.</div>Y492zhuhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Describtion_of_Text_Mining&diff=46526Describtion of Text Mining2020-11-26T03:38:58Z<p>Y492zhu: /* Classification */</p>
<hr />
<div>== Presented by == <br />
Yawen Wang, Danmeng Cui, Zijie Jiang, Mingkang Jiang, Haotian Ren, Haris Bin Zahid<br />
<br />
== Introduction ==<br />
This paper focuses on the different text mining tasks and the existence of text mining in healthcare and biomedical domains. The text mining field has been popular as a result of the amount of text data that is available in different forms. The text data is bound to grow even more in 2020, indicating a 50 times growth since 2010. Text is a kind of unstructured information, which is easy for humans to construct and understand, but it is difficult for machines. Hence, there is a need to design algorithms to effectively process this avalanche of text. To further explore the text mining field, the related text mining approaches can be considered. The different text mining approaches relate to two main methods: knowledge delivery and traditional data mining methods. <br />
<br />
The authors note that knowledge delivery methods involve the application of different steps to a specific data set to create specific patterns. Research in knowledge delivery methods has evolved over the years due to advances in hardware and software technology. On the other hand, data mining has experienced substantial development through the intersection of three fields: databases, machine learning, and statistics. As brought out by the authors, text mining approaches focus on the exploration of information from a specific text. The information explored is in the form of structured, semi-structured, and unstructured text. It is important to note that text mining covers different sets of algorithms and topics that include information retrieval. The topics and algorithms are used for analyzing different text forms.<br />
<br />
== Text Representation and Encoding ==<br />
In this section of the paper, the authors explore the different ways in which the text can be represented on a large collection of documents. One common way of representing the documents is in the form of a bag of words. The bag of words considers the occurrences of different terms. In different text mining applications, documents are ranked and represented as vectors so as to display the significance of any word. The authors note that the three basic models used are vector space, inference network, and the probabilistic models. The vector space model is used to represent documents by converting them into vectors. In the model, a variable is used to represent each model to indicate the importance of the word in the document. The words are weighted using the TF-IDF scheme computed as <br />
<br />
$$<br />
q(w)=f_d(w)*log{\frac{|D|}{f_D(w)}}<br />
$$<br />
<br />
In many text mining algorithms, one of the key components is preprocessing. Preprocessing consists of different tasks that include filtering, tokenization, stemming, and lemmatization. The first step is tokenization, where a character sequence is broken down into different words or phrases. After the breakdown, filtering is carried out to remove some words. The various word inflected forms are grouped together through lemmatization, and later, the derived roots of the derived words are obtained through stemming.<br />
<br />
== Classification ==<br />
Classification in Text Mining aims to assigned predefined classes to text documents. For a set <math>\mathcal{D} = {d_1, d_2, ... d_n}</math> of documents, such that each <math>d_i</math> is mapped to a label <math>l_i</math> from the set <math>\mathcal{L} = {l_1, l_2, ... l_k}</math>. The goal is to find a classification model <math>f</math> such that: <math>\\</math><br />
$$<br />
f: \mathcal{D} \rightarrow \mathcal{L} \quad \quad \quad f(\mathcal{d}) = \mathcal{l}<br />
$$<br />
The author illustrates 4 different classifiers that are commonly used in text mining.<br />
<br />
<br />
'''1. Naive Bayes Classifier''' <br />
<br />
Bayes rule is used to classify new examples and select the class that is most has the generated result. <br />
Naive Bayes Classifier models the distribution of documents in each class using a probabilistic model assuming that the distribution<br />
of different terms is independent of each other. The models commonly used in this classifier tried to find the posterior probability of a class based on the distribution and assumes that the documents generated are based on a mixture model parameterized by <math>\theta</math> and compute the likelihood of a document using the sum of probabilities over all mixture component.<br />
<br />
'''2. Nearest Neighbour Classifier'''<br />
<br />
Nearest Neighbour Classifier uses distance-based measures to perform the classification. The documents which belong to the same class are more likely "similar" or close to each other based on the similarity measure. The classification of the test documents is inferred from the class labels of similar documents in the training set.<br />
<br />
'''3. Decision Tree Classifier'''<br />
<br />
A hierarchical tree of the training instances, in which a condition on the attribute value is used to divide the data hierarchically. The decision tree recursively partitions the training data set into smaller subdivisions based on a set of tests defined at each node or branch. Each node of the tree is a test of some attribute of the training instance, and each branch descending from the node corresponds to one of the values of this attribute. The conditions on the nodes are commonly defined by the terms in the text documents.<br />
<br />
'''4. Support Vector Machines'''<br />
<br />
SVM is a form of Linear Classifiers which are models that makes a classification decision based on the value of the linear combinations of the documents features. The output of a linear predictor is defined to the <math> y=\vec{a} \cdot \vec{x} + b</math> where <math>\vec{x}</math> is the normalized document word frequency vector, <math>\vec{a}</math> is a vector of coeddifients and <math>b</math> is a scalar. Support Vector Machines attempts to find a linear separators between various classes. An advantage of the SVM method is it is robust to high dimensionality.<br />
<br />
== Clustering ==<br />
Clustering has been extensively studied in the context of the text as it has a wide range of applications such as visualization and document organization.<br />
<br />
== Information Extraction ==<br />
Information Extraction (IE) is the process of extracting useful, structured information from unstructured or semi-structured text. It automatically extracts based on our command. <br />
<br />
For example, consider the following sentence, “XYZ company was founded by Peter in the year of 1950”<br />
We can identify the following information:<br />
<br />
Founderof(Peter, XYZ)<br />
Foundedin(1950, XYZ)<br />
<br />
The author mentioned 4 parts that are important for Information Extraction<br />
<br />
'''1. Namely Entity Recognition(NER)'''<br />
This is the process of identifying real-world entity from free text, such as "Apple Inc.", "Donald Trump", "PlayStation 5" etc. Moreover, the task is to identify the category of these entities, such as "Apple Inc." is in the category of the company, "Donald Trump" is in the category of the USA president, and "PlayStation 5" is in the category of the entertainment system. <br />
<br />
'''2. Hidden Markov Model'''<br />
Since traditional probabilistic classification does not consider the predicted labels of neighbor words, we use the Hidden Markov Model when doing Information Extraction. This model is different because it considers the label of one word depends on the previous words that appeared. <br />
<br />
'''3. Conditional Random Fields'''<br />
This is a technique that is widely used in Information Extraction. The definition of it is related to graph theory. <br />
let G = (V, E) be a graph and Yv stands for the index of the vertices in G. Then (X, Y) is a conditional random field, when the random variables Yv, conditioned on X, obey Markov property with respect to the graph, and:<br />
p(Yv |X, Yw ,w , v) = p(Yv |X, Yw ,w ∼ v), where w ∼ v means w and v are neighbors in G.<br />
<br />
'''4. Relation Extraction'''<br />
This is a task of finding semantic relationships between word entities in text documents. Such as "Seth Curry" is the brother of "Stephen Curry", if there is a document including these two names, the task is to identify the relationship of these two entities.<br />
<br />
== Biomedical Application ==<br />
<br />
Text mining has several applications in the domain of biomedical sciences. The explosion of academic literature in the field has made it quite hard for scientists to keep up with novel research. This is why text mining techniques are ever so important in making the knowledge digestible.<br />
<br />
The text mining techniques are able to extract meaningful information from large data by making use of biomedical ontology, which is a compilation of a common set of terms used in an area of knowledge. The Unified Medical Language System (UMLS) is the most comprehensive such resource, consisting of definitions of biomedical jargon. Several information extraction algorithms rely on the ontology to perform tasks such as Named Entity Recognition (NER) and Relation Extraction.<br />
<br />
NER involves locating and classifying biomedical entities into meaningful categories and assigning semantic representation to those entities. The NER methods can be broadly grouped into Dictionary-based, Rule-based and Statistical approaches. Relation extraction, on the other hand, is the process of determining relationships between the entities. This is accomplished mainly by identifying the correlation between entities through analyzing the frequency of terms, as well as rules defined by domain experts. Moreover, modern algorithms are also able to summarize large documents and answer natural language questions posed by humans.<br />
<br />
== Conclusion ==<br />
<br />
This paper gave a holistic overview of the methods and applications of text mining, particularly its relevance in the biomedical domain. It highlights several popular algorithms along with their limitations. Because of ever-growing data, the interest in this field is massive and is bound to grow in the future.<br />
<br />
== References ==<br />
<br />
Allahyari, M., Pouriyeh, S., Assefi, M., Safaei, S., Trippe, E. D., Gutierrez, J. B., & Kochut, K. (2017). A brief survey of text mining: Classification, clustering, and extraction techniques. arXiv preprint arXiv:1707.02919.</div>Y492zhuhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:Yktan&diff=46507User:Yktan2020-11-26T03:24:19Z<p>Y492zhu: /* Introduction */</p>
<hr />
<div>== Presented by == <br />
Ruixian Chin, Yan Kai Tan, Jason Ong, Wen Cheen Chiew<br />
<br />
== Introduction ==<br />
<br />
Much of the success in training deep neural networks (DNNs) is thanks to the collection of large datasets with human annotated labels. However, human annotation is both a time-consuming and expensive task, especially for the data that requires expertise such as medical data. Furthermore, certain datasets will be noisy due to the biases introduced by different annotators.<br />
<br />
There are a few existing approaches to use datasets with noisy labels. In learning with noisy labels (LNL), most methods take a loss correction approach. An example of a popular loss correction approach is the bootstrapping loss approach. Another approach to reduce annotation cost is semi-supervised learning (SSL), where the training data consists of labeled and unlabeled samples.<br />
<br />
This paper introduces DivideMix, which combines approaches from LNL and SSL. One unique thing about DivideMix is that it discards sample labels that are highly likely to be noisy and leverages these noisy samples as unlabeled data instead. This prevents the model from overfitting and improves generalization performance. Key contributions of this work are:<br />
1) Co-divide, which trains two networks simultaneously, aims to improve generalization and avoiding confirmation bias.<br />
2) During SSL phase, an improvement is made on an existing method (MixMatch) by combining it with another method (MixUp).<br />
3) Significant improvements to state-of-the-art results on multiple conditions are experimentally shown while using DivideMix. Extensive ablation study and qualitative results are also shown to examine the effect of different components.<br />
<br />
== Motivation ==<br />
<br />
While much has been achieved in training DNNs with noisy labels and SSL methods individually, not much progress has been made in exploring their underlying connections and building on top of the two approaches simultaneously. <br />
<br />
Existing LNL methods aim to correct the loss function by:<br />
<ol><br />
<li> Treating all samples equally and correcting loss explicitly or implicitly through relabeling of the noisy samples<br />
<li> Reweighting training samples or separating clean and noisy samples, which results in correction of the loss function<br />
</ol><br />
<br />
A few examples of LNL methods include:<br />
<ol><br />
<li> Estimating the noise transition matrix to correct the loss function<br />
<li> Leveraging DNNs to correct labels and using them to modify the loss<br />
<li> Reweighting samples so that noisy labels contribute less to the loss<br />
</ol><br />
<br />
However, these methods each have some downsides. For example, it is very challenging to correctly estimate the noise transition matrix in the first method; for the second method, DNNs tend to overfit to datasets with high noise ratio; for the third method, we need to be able to identify clean samples, which has also proven to be challenging.<br />
<br />
On the other hand, SSL methods mostly leverage unlabeled data using regularization to improve model performance. A recently proposed method, MixMatch incorporates the two classes of regularization – consistency regularization which enforces the model to produce consistent predictions on augmented input data, and entropy minimization which encourages the model to give high-confidence predictions on unlabeled data, as well as MixUp regularization. <br />
<br />
DivideMix partially adopts LNL in that it removes the labels that are highly likely to be noisy by using co-divide to avoid the confirmation bias problem. It then utilizes the noisy samples as unlabeled data and adopts an improved version of MixMatch (SSL) which accounts for the label noise during the label co-refinement and co-guessing phase. By incorporating SSL techniques into LNL and taking the best of both worlds, DivideMix aims to produce highly promising results in training DNNs by better addressing the confirmation bias problem, more accurately distinguishing and utilizing noisy samples, and performing well under high levels of noise.<br />
<br />
== Model Architecture ==<br />
<br />
DivideMix leverages semi-supervised learning to achieve effective modeling. The sample is first split into a labeled set and an unlabeled set. This is achieved by fitting a Gaussian Mixture Model as a per-sample loss distribution. The unlabeled set is made up of data points with discarded labels deemed noisy. Then, to avoid confirmation bias, which is typical when a model is self-training, two models are being trained simultaneously to filter error for each other. This is done by dividing the data using one model and then training the other model. This algorithm, known as Co-divide, keeps the two networks from converging when training, which avoids the bias from occurring. Figure 1 describes the algorithm in graphical form.<br />
<br />
[[File:ModelArchitecture.PNG | center]]<br />
<br />
<div align="center">Figure 1: Model Architecture of DivideMix</div><br />
<br />
For each epoch, the network divides the dataset into a labeled set consisting of clean data, and an unlabeled set consisting of noisy data, which is then used as training data for the other network, where training is done in mini-batches. For each batch of the labelled samples, co-refinement is performed by using the ground truth label <math> y_b </math>, the predicted label <math> p_b </math>, and the posterior is used as the weight, <math> w_b </math>. <br />
<br />
<center><math> \bar{y}_b = w_b y_b + (1-w_b) p_b </math></center> <br />
<br />
Then, a sharpening function is implemented on this weighted sum to produce the estimate, <math> \hat{y}_b </math>. Using all these predicted labels, the unlabeled samples will then be assigned a "co-guessed" label, which should produce a more accurate prediction. Having calculated all these labels, MixMatch is applied to the combined mini-batch of labeled, <math> \hat{X} </math> and unlabeled data, <math> \hat{U} </math>, where, for a pair of samples and their labels, one new sample and new label is produced. More specifically, for a pair of samples <math> (x_1,x_2) </math> and their labels <math> (p_1,p_2) </math>, the mixed sample <math> (x',p') </math> is:<br />
<br />
<center><br />
<math><br />
\begin{alignat}{2}<br />
<br />
\lambda &\sim Beta(\alpha, \alpha) \\<br />
\lambda ' &= max(\lambda, 1 - \lambda) \\<br />
x' &= \lambda ' x_1 + (1 - \lambda ' ) x_2 \\<br />
p' &= \lambda ' p_1 + (1 - \lambda ' ) p_2 \\<br />
<br />
\end{alignat}<br />
</math><br />
</center> <br />
<br />
MixMatch transforms <math> \hat{X} </math> and <math> \hat{U} </math> into <math> X' </math> and <math> U' </math>. Then, the loss on <math> X' </math>, <math> L_X </math> (Cross-entropy loss) and the loss on <math> U' </math>, <math> L_U </math> (Mean Squared Error) are calculated. A regularization term, <math> L_{reg} </math>, is introduced to regularize the model's average output across all samples in the mini-batch. Then, the total loss is calculated as:<br />
<br />
<center><math> L = L_X + \lambda_u L_U + \lambda_r L_{reg} </math></center> ,<br />
<br />
where <math> \lambda_r </math> is set to 1, and <math> \lambda_u </math> is used to control the unsupervised loss.<br />
<br />
Lastly, the stochastic gradient descent formula is updated with the calculated loss, <math> L </math>, and the estimated parameters, <math> \boldsymbol{ \theta } </math>.<br />
<br />
== Results ==<br />
'''Applications'''<br />
<br />
The method was validated using four benchmark datasets: CIFAR-10, CIFAR100 (Krizhevsky & Hinton, 2009)(both contain 50K training images and 10K test images of size 32 × 32), Clothing1M (Xiao et al., 2015), and WebVision (Li et al., 2017a).<br />
Two types of label noise are used in the experiments: symmetric and asymmetric.<br />
An 18-layer PreAct Resnet (He et al., 2016) is trained using SGD with a momentum of 0.9, a weight decay of 0.0005, and a batch size of 128. The network is trained for 300 epochs. The initial learning rate was set to 0.02, and reduced by a factor of 10 after 150 epochs. Before applying the Co-divide and MixMatch strategies, the models were first independently trained over the entire dataset using cross-entropy loss during a "warm-up" period. Initially, training the models in this way prepares a more regular distribution of losses to improve upon in subsequent epochs. The warm-up period is 10 epochs for CIFAR-10 and 30 epochs for CIFAR-100. For all CIFAR experiments, we use the same hyperparameters M = 2, T = 0.5, and α = 4. τ is set as 0.5 except for 90% noise ratio when it is set as 0.6.<br />
<br />
<br />
'''Comparison of State-of-the-Art Methods'''<br />
<br />
The effectiveness of DivideMix was shown by comparing the test accuracy with the most recent state-of-the-art methods: <br />
Meta-Learning (Li et al., 2019) proposes a gradient-based method to find model parameters that are more noise-tolerant; <br />
Joint-Optim (Tanaka et al., 2018) and P-correction (Yi & Wu, 2019) jointly optimize the sample labels and the network parameters;<br />
M-correction (Arazo et al., 2019) models sample loss with BMM and apply MixUp.<br />
The following are the results on CIFAR-10 and CIFAR-100 with different levels of symmetric label noise ranging from 20% to 90%. Both the best test accuracy across all epochs and the averaged test accuracy over the last 10 epochs were recorded in the following table:<br />
<br />
<br />
[[File:divideMixtable1.PNG | center]]<br />
<br />
From table1, the author noticed that none of these methods can consistently outperform others across different datasets. M-correction excels at symmetric noise, whereas Meta-Learning performs better for asymmetric noise. DivideMix outperforms state-of-the-art methods by a large margin across all noise ratios. The improvement is substantial (∼10% of accuracy) for the more challenging CIFAR-100 with high noise ratios.<br />
<br />
DivideMix was compared with the state-of-the-art methods with the other two datasets: Clothing1M and WebVision. It also shows that DivideMix consistently outperforms state-of-the-art methods across all datasets with different types of label noise. For WebVision, DivideMix achieves more than 12% improvement in top-1 accuracy. <br />
<br />
<br />
'''Ablation Study'''<br />
<br />
The effect of removing different components to provide insights into what makes DivideMix successful. We analyze the results in Table 5 as follows.<br />
<br />
<br />
[[File:DivideMixtable5.PNG | center]]<br />
<br />
The authors find that both label refinement and input augmentation are beneficial for DivideMix.<br />
<br />
== Conclusion ==<br />
<br />
This paper provides a new and effective algorithm for learning with noisy labels by leveraging SSL. The DivideMix method trains two networks simultaneously and utilizes co-guessing and co-labeling effectively, therefore it is a robust approach to dealing with noise in datasets. DivideMix has also been tested using various datasets with the results consistently being one of the best when compared to other advanced methods.<br />
<br />
Future work of DivideMix is to create an adaptation for other applications such as Natural Language Processing, and incorporating the ideas of SSL and LNL into DivideMix architecture.<br />
<br />
== Critiques ==<br />
<br />
While combining both models makes the result better, the author did not show the relative time increase using this new combined methodology, which is very crucial considering training a large amount of data, especially for images. In addition, it seems that the author did not perform much on hyperparameters tuning for the combined model.<br />
<br />
== References ==<br />
Eric Arazo, Diego Ortego, Paul Albert, Noel E. O’Connor, and Kevin McGuinness. Unsupervised<br />
label noise modeling and loss correction. In ICML, pp. 312–321, 2019.<br />
<br />
David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, and Colin<br />
Raffel. Mixmatch: A holistic approach to semi-supervised learning. NeurIPS, 2019.<br />
<br />
Yifan Ding, Liqiang Wang, Deliang Fan, and Boqing Gong. A semi-supervised two-stage approach<br />
to learning from noisy labels. In WACV, pp. 1215–1224, 2018.</div>Y492zhu