Unsupervised Domain Adaptation with Residual Transfer Networks

Introduction

Domain Adaptation [1]is a problem in machine learning which involves taking a model which has been trained on a source domain and applying this to a different (but related) target domain. Unsupervised domain adaptation refers to the situation in which the source data is labeled, while the target data is (predominantly) unlabeled. This scenario arises when we aim at learning from a source data distribution a well-performing model on a different (but related) target data distribution. For instance, one of the tasks of the common spam filtering problem consists in adapting a model from one user (the source distribution) to a new one who receives significantly different emails (the target distribution). Note that, when more than one source distribution is available the problem is referred to as multi-source domain adaptation The problem at hand is then finding ways to generalize the learning on the source domain to the target domain. In the age of deep networks, this problem has become particularly salient due to the need for vast amounts of labeled training data, in order to reap the benefits of deep learning. Manual generation of labeled data is often prohibitive, and in absence of such data networks are rarely performant. The attempt to circumvent this drought of data typically necessitates the gathering of "off-the-shelf" data sets, which are tangentially related and contain labels, and then building models in these domains. The fundamental issue that unsupervised domain adaptation attempts to address is overcoming the inherent shift in distribution across the domains, without the ability to observe this shift directly. The goal of this paper is to simultaneously learn adaptive classifiers and transferable features from labeled data in the source domain and unlabeled data in the target domain by embedding the adaptations of both classifiers and features in a unified deep architecture.

This paper proposes a method for unsupervised domain adaptation which relies on three key components:

1. A kernel-based penalty to ensure that the abstract representations generated by the networks hidden layers are similar between the source and the target data;
2. An entropy based penalty on the target classifier, which exploits the entropy minimization principle; and
3. A residual network structure is appended, which allows the source and target classifiers to differ by a (learned) residual function, thus relaxing the shared classifier assumption which is traditionally made.

This method outperforms state-of-the-art techniques on common benchmark datasets and is flexible enough to be applied in most feed-forward neural networks.

The Office-31 Dataset Images for Backpack. Shows the variation in the source and target domains to motivate why these methods are important.

Working Example (Office-31)

In order to assist in the understanding of the methods, it is helpful to have a tangible sense of the problem front of mind. The Domain Adaptation Project [2] provides data sets which are tailored to the problem of unsupervised domain adaptation. One of these datasets (which is later used in the experiments of this paper) has images which are labeled based on the Amazon product page for the various items. There are then corresponding pictures taken either by webcams or digital SLR cameras. The goal of unsupervised domain adaptation on this data set would be to take any of the three image sources as the source domain, and transfer a classifier to the other domain; see the example images to understand the differences.

One can imagine that, while it is likely easy to scrape labeled images from Amazon, it is likely far more difficult to collect labeled images from webcam or DSLR pictures directly. The ultimate goal of this method would be to train a model to recognize a picture of a backpack taken with a webcam, based on images of backpacks scraped from Amazon (or similar tasks).

Related Work

Broadly speaking, the problem of domain adaptation mitigates manual labeling of data in areas such as machine learning, computer vision, and natural language processing. The general goal of domain adaptation is to reduce the discrepancy in probability distributions between the source and target domains.

Research into the use of Deep Neural Networks for the purpose of domain adaptation has suggested that, while networks learn abstract feature representations which can reduce the discrepancy across domains, it is not possible to wholly remove it [3], [4]. Further work has been done to design networks which adapt traditional deep nets (typically CNNs) to specifically address the problems posed by domain adaptation, these methods all only address the issue of feature adaptation [5], [6], [7]. That is, they all assume that the target and source classifiers are shared between domains.

The authors drew particular motivation from He et al. Their work involve deep residual learning to ease the training of very deep networks that have hundreds of layers. [8] with the proposed structure of residual networks. Combining the insights from the ResNet architecture, in addition to previous work that had leveraged classifier adaptation (in the context where some target data is labeled) [9], [10], [11] the authors develop their proposed network.

Residual Transfer Networks

The challenge of unsupervised domain adaptation arises in that the target domain has no labeled data, while the source classifier $f_s$ trained on source domain cannot be directly applied to the target domain due to the distribution discrepancy. Thus, a joint adaptation of features and classifiers can be used to enable effective domain adaptation. This paper presents an end-to-end deep learning framework for classifier adaptation which is harder in the sense that the target domain is fully unlabeled. The authors also propose a method for feature adaptation using Maximum Mean Discrepancy (MMD).

Generally, in an unsupervised domain adaptation problem, we are dealing with a set $\mathcal{D}_s$ (called the source domain) which is defined by $\{(x_i^s, y_i^s)\}_{i=1}^{n_s}$. That is the set of all labeled input-output pairs in our source data set. We denote the number of source elements by $n_s$. There is a corresponding set $\mathcal{D}_t = \{(x_i^t)\}_{i=1}^{n_t}$ (the target domain), consisting of unlabeled input values. There are $n_t$ such values.

The overarching structure of the RTN. Consists of an existing network, to which a bottleneck, MMD block, and residual block is appended.

We can think of $\mathcal{D}_s$ as being sampled from some underlying distribution $p$, and $\mathcal{D}_t$ as being sampled from $q$. Generally, we have that $p \neq q$, partially motivating the need for domain adaptation methods.

We can consider the classifiers $f_s(\underline{x})$ and $f_t(\underline{x})$, for the source domain and target domain respectively. It is possible to learn $f_s$ based on the sample $\mathcal{D}_s$. Under the shared classifier assumption it would be the case that $f_s(\underline{x}) = f_t(\underline{x})$, and thus learning the source classifier is enough. This method relaxes this assumption, assuming that in general $f_s \neq f_t$, and attempting to learn both.

The example network extends deep convolutional networks (in this case AlexNet [12]) to Residual Transfer Networks, the mechanics of which are outlined below. Recall that, if $L(\cdot, \cdot)$ is taken to be the cross-entropy loss function, then the empirical error of a CNN on the source domain $\mathcal{D}_s$ is given by:

[math]\displaystyle{ \min_{f_s} \frac{1}{n_s} \sum_{i=1}^{n_s} L(f_s(x_i^s), y_i^s) }[/math]

In a standard implementation, the CNN optimizes over the above loss. This will be the starting point for the RTN.

Structural Overview

The model proposed in this paper extends existing CNN's and alters the loss function that is optimized over. While each of these components is discussed in depth below, the overarching architecture involves four components:

1. An existing deep model. While this can be any model, in theory, the authors leverage AlexNet in practice.
2. A bottleneck layer used to reduce the dimensionality of the learned abstract feature space, directly after the existing network.
3. An MMD block, with the expressed intention of feature adaptation.
4. A residual block, with the expressed intention of classifier adaptation.

This structure is then optimized for a loss function which combines the standard cross-entropy penalty with MMD and target entropy penalties, yielding the proposed Residual Transfer Network (RTN) structure.

Feature Adaptation

Feature adaptation refers to the process in which the features which are learned to represent the source domain are made applicable to the target domain. Broadly speaking a CNN works to generate abstract feature representations of the distribution that the inputs are sampled from. It has been found that using these deep features can reduce, but not remove, cross-domain distribution discrepancy, hence the need for the feature adaptation. It is important to note that CNN's transfer from general to specific features as the network gets deeper. In this light, the discrepancy between the feature representation of the source and the target will grow through a deeper convolutional net. As such a technique for forcing these distributions to be similar is needed.

In particular, the authors of this paper perform feature adaptation by matching the feature distributions of multiple layers $l ∈ L$ across domains. They impose a bottleneck layer (call it $fc_b$) which is included after the final convolutional layer of AlexNet. This dense layer is connected to an additional dense layer $fc_c$, (which will serve as the target classification layer). They then compute the tensor product between the activations of the layers, performing "lossless multi-layer feature fusion". That is for the source domain they define $z_i^s \overset{\underset{\mathrm{def}}{}}{=} x_i^{s,fc_b}\otimes x_i^{s,fc_c}$ and for the target domain, $z_i^t \overset{\underset{\mathrm{def}}{}}{=} x_i^{t,fc_b}\otimes x_i^{t,fc_c}$. Feature fusion is the process of combining two feature vectors to obtain a single feature vector, which is more discriminative than any of the input feature vectors. The authors then employ feature adaptation by means of Maximum Mean Discrepancy, between the source and target domains, on these fusion features.

The Maximum Mean Discrepancy Block (MMD) included in the RTN. The outputs of $fc_b$ and $fc_c$ are fused through a tensor product, and then passed through the MMD penalty, ensuring distributional similarity.

Maximum Mean Discrepancy

In unsupervised learning, we are given independent samples $x_i$ from some underlying data distribution $P$, and our goal is to come up with an approximate distribution $Q$ that is as close to $P$ as possible, only using the samples $x_i$. Often, $Q$ is chosen from a parametric family of distributions ${Q(⋅; \theta), \quad \theta \in \Theta}$, and our goal is to find the optimal parameters $\theta*$ so that the distribution $P$ is best approximated. The central issue of unsupervised learning is choosing an appropriate objective function $l(\theta,P)$, that appropriately measures the quality of our approximation, and which is tractable to compute and optimise when we are working with complicated, deep models. MMD is one such loss function that allowing to match $P$ and $Q$ in unsupervised settings and it is also applicable in supervised domain adaptation scenario as well.

Maximum mean discrepancy(MMD) was originally proposed by the kernel machines community as a nonparametric way to measure dissimilarity between two probability distributions. MMD is a Kernel method that involves mapping to a Reproducing Kernel Hilbert Space (RKHS) [13]. Denote the RKHS $\mathcal{H}_K$ with a characteristic kernel $K$. We then define the mean embedding of a distribution $p$ in $\mathcal{H}_K$ to be the unique element $\mu_K(p)$ such that $\mathbf{E}_{x\sim p}f(x) = \langle f(x), \mu_K(p)\rangle_{\mathcal{H}_K}$ for all $f \in \mathcal{H}_K$. Now, if we take $\phi: \mathcal{X} \to \mathcal{H}_K$, then we can define the MMD between two distributions $p$ and $q$ as follows:

[math]\displaystyle{ d_k(p, q) \overset{\underset{\mathrm{def}}{}}{=} ||\mathbf{E}_{x\sim p}(\phi(x^s)) - \mathbf{E}_{x\sim q}(\phi(x^t))||_{\mathcal{H}_K} }[/math]

Effectively, the MMD will compute the self-similarity of $p$ and $q$, and subtract twice the cross-similarity between the distributions: $\widehat{\text{MMD}}^2 = \text{mean}(K_{pp}) + \text{mean}(K_{qq}) - 2\times\text{mean}(K_{pq})$. From here we can infer that $p$ and $q$ are equivalent distributions if and only if the $\text{MMD} = 0$. If we then wish to force two distributions to be similar, this becomes a minimization problem over the MMD.

MMD is very similar to the adversarial loss in many ways discussed in much details in this blog post, however, one striking difference is that maximisation of MMD can be carried out analytically by applying kernel trick, and we obtain the following expression:

$$ MMD_k(Q,P)=E_{x,x^′~P,P}k(x,x^′) + E_{x,x^′∼Q,Q}k(x,x^′) - 2E_{x,x^′∼P,Q}k(x,x^′) $$

In the expression above

• $E_{x,x^′~P,P}k(x,x^′)$ is constant with respect to Q, so we can just drop it from the objective.
• The second term $E_{x,x^′∼Q,Q}k(x,x^′)$ can be interpreted as an entropy term: minimising this will force $Q$ to be spread out, rather than concentrate on a single set of points
• The third term $E_{x,x^′∼P,Q}k(x,x^′)$ ensures that samples from Q are on average close to samples from P

Two important notes:

1. The RKHS, and as such MMD, depend on the choice of the kernel;
2. Computing the MMD efficiently requires an unbiased estimate of the MMD (as outlined [14]).

MMD for Feature Adaptation in the RTN

The authors wish to minimize the MMD between the fusion features outlined above derived from the source and target domains. Concretely this amounts to forcing the distribution of the abstract representation of the source domain $\mathcal{D}_s$ to be similar to the distribution of the abstract representation of the target domain $\mathcal{D}_t$. Performing this optimization over the fused features between the $fb_b$ and $fb_c$ forces each of those layers towards similar distributions.

Practically this involves an additional penalty function given by the following:

[math]\displaystyle{ D_{\mathcal{L}}(\mathcal{D}_s, \mathcal{D}_t) = \sum_{i,j=1}^{n_s} \frac{k(z_i^s, z_j^s)}{n_s^2} + \sum_{i,j=1}^{n_t} \frac{k(z_i^t, z_j^t)}{n_t^2} -2 \sum_{i=1}^{n_s}\sum_{j=1}^{n_t} \frac{k(z_i^s, z_j^t)}{n_sn_t} }[/math]

Where the characteristic kernel $k(z, z')$ is the Gaussian kernel, defined on the vectorization of tensors, with bandwidth parameter $b$. That is: $k(z, z') = \exp(-||vec(z) - vec(z')||^2/b)$.

Classifier Adaptation

In traditional unsupervised domain adaptation, there is a shared-classifier assumption which is made. In essence, if $f_s(x)$ represents the classifier on the source domain, and $f_t(x)$ represents the classifier on the target domain then this assumption simply states that $f_s = f_t$. While this may seem to be a reasonable assumption at first glance, it is problematic largely in that this is an assumption that is incredibly difficult to check. If it could be readily confirmed that the source and target classifiers could be shared, then the problem of domain adaptation would be largely trivialized. Instead, the authors here relax this assumption slightly. They postulate that instead of being equivalent, the source and target classifier differ by some perturbation function $\Delta f$. The general idea is that, by assuming $f_S(x) = f_T(x) + \Delta f(x)$, where $f_S$ and $f_T$ correspond to the source and target classifiers, pre-activation, and $\Delta f(x)$ is some residual function dependent on both the target classifier $f_T(x)$ (due to the functional dependency) as well as the source classifier $f_S(x)$ (due to the back-propagation pipeline). The authors also argue that the perturbation function $\Delta f(x)$ can be learned jointly from the source labeled data and target unlabeled data.

The authors then suggest using residual blocks, as popularized by the ResNet framework [15], to learn this residual function.

A comparison of a standard Deep Neural Network block which is designed to fit a function H(x) compared to a residual block which fits H(x) as the sum of the input, x, and a learned residual function, F(X).

Residual Networks Framework

A (Deep) Residual Network, as proposed initially in ResNet, employs residual blocks to assist in the learning process and were a key component of being able to train extraordinarily deep networks. The Residual Network is comprised largely in the same manner as standard neural networks, with one key difference, namely the inclusion of residual blocks - sets of layers which aim to estimate a residual function in place of estimating the function itself.

That is, if we wish to use a DNN to estimate some function $h(x)$, a residual block will decompose this to $h(x) = F(x) + x$. The layers are then used to learn $F(x)$, and after the layers which aim to learn this residual function, the input $x$ is recombined through element-wise addition, to form $h(x) = F(x) + x$. This was initially proposed as a manner to allow for deeper networks to be effectively trained but has since used in novel contexts.

Residual Blocks in the RTN

The Structure of the Residual Block in the RTN framework. The block relies on two additional dense layers following the target classifier in an attempt to learn the residual difference between the source and target classifiers.

The authors leverage residual blocks for the purpose of classifier adaptation. Operating under the assumption that the source and target classifiers differ by an arbitrary perturbation function, $f(x)$, the authors add an additional set of densely connected layers which the source data will flow through. In particular, the authors take the $fc_c$ layer above as the desired target classifier. For the source data an additional set of layers ($fc-1$ and $fc-2$) are added following $fc_c$, which are connected as a residual block. The output of the classifier layer is then added back to the output of the residual block in order to form the source classifier.

It is necessary to note that in this case, the output from $fc_c$ passes the non-activated (i.e. pre-softmax activation) to the element-wise addition, the result of which is passed through the activation layer, yielding the source prediction. In the provided diagram, we have that $f_S(x)$ represents the non-activated output from the additive layer in the residual block; $f_T(x)$ represents the non-activated output from the target classifier; and $fc-1$/$fc-2$ are used to learn the perturbation function $\Delta f(x)$.

Entropy Minimization

In addition to the residual blocks, the authors make use of the entropy minimization principle [16], which was originally introduced in for semi-supervised learning, to further refine the classifier adaptation. In particular, by minimizing the entropy of the target classifier (or more correctly, the entropy of the class conditional distribution $f_j^t(x_i^t) = p(y_i^t = j \mid x_i^t; f_t)$), low-density separation between the classes is encouraged. Low-Density Separation is a concept used predominantly in semi-supervised learning, which in essence tries to draw class decision boundaries in regions where there are few data points (labeled or unlabeled). The above paper leverages an entropy regularization scheme to achieve the goal low-density separation goal; this is adopted here to the case of unsupervised domain adaptation.

In practice this amounts to adding a further penalty based on the entropy of the class conditional distribution. In particular, if $H(\cdot)$ is defined to be the entropy function, such that $H(f_t(x_i^t)) = - \sum_{j=1}^c f_j^t(x_i^t)\log f_j^t(x_i^t)$, where $c$ is the number of classes and $f_j^t(x_i^t)$ represents the probability of predicting class $j$ for point $x_i^t$, then over the target domain $\mathcal{D}_t$ we define the entropy penalty to be:

[math]\displaystyle{ \frac{1}{n_t} \sum_{i=1}^{n_t} H(f_t(x_i^t)) }[/math]

The combination of the residual learning and the entropy penalty, the authors hypothesize will enable effective classifier adaptation.

Residual Transfer Network

The combination of the MMD loss introduced in feature adaptation, the residual block introduced in classifier adaptation, and the application of the entropy minimization principle cumulates in the Residual Transfer Network proposed by the authors. The model will be optimized according to the following loss function, which combines the standard cross-entropy, MMD penalty, and entropy penalty:

[math]\displaystyle{ \min_{f_s = f_t + \Delta f} \underbrace{\left(\frac{1}{n_s} \sum_{i=1}^{n_s} L(f_s(x_i^s), y_i^s)\right)}_{\text{Typical Cross-Entropy}} + \underbrace{\frac{\gamma}{n_t}\left(\sum_{i=1}^{n_t} H(f_t(x_i^t)) \right)}_{\text{Target Entropy Minimization}} + \underbrace{\lambda\left(D_{\mathcal{L}}(\mathcal{D}_s, \mathcal{D}_t)\right)}_{\text{MMD Penalty}} }[/math]

Where we take $\gamma$ and $\lambda$ to be tradeoff parameters between the entropy penalty and the MMD penalty. As classifier adaptation proposed in this paper and feature adaptation studied in [5, 6] are tailored to adapt different layers of deep networks, they are expected to complement each other and to establish better performance.

The full network, which is trained subject to the above optimization problem, thus takes on the following structure.

Experiments

Set-up

The performance of RTN was jointly compared across two key data sets in the area of Unsupervised Domain Adaptation. Specifically, Office-31 (discussed in the introduction) and Office-Caltech (maintained by the same project group). Office-31 is comprised of images from 3 sources, Amazon (A), Webcam (W), and DSLR (D), of 31 different objects. Office-Caltech is derived by considering 10 classes common to both the Office-31 and the Caltech data sets, thus providing further adaptation possibilities. This provides 6 Transfer Tasks on the 31 classes of Office-31 ($\{(A,W), (A,D), (W,A), (W,D), (D,A), (D,W)\}$) and 12 Transfer Tasks on the 10 classes of Office-Caltech ($\{(A,W), (A,D), (A,C), (W,A), (W,D), (W,C), (D,A), (D,W), (D,C), (C,A), (C,W), (C,D)\}$).

The authors then compare the results on the 18 different adaptation tasks against 6 other models. In order to determine the efficacy of the various contributions outlined in the paper, they perform an ablation study, evaluating variants of the RTN. Specifically, they consider the RTN with only the MMD module (RTN (mmd)), the RTN with the MMD module and the entropy minimization (RTN (mmd+ent)), and the complete RTN (RTN (mmd+ent+res)). The experiments leverage all the labeled training data and compute accuracy across all unlabeled domain data. The parameters of the model (i.e. $\gamma$, and $\lambda$) are fixed based on a single validation point on the transfer task $\mathbf{A}\to\mathbf{W}$. These parameters are then maintained across all transfer tasks.

As for specification details, the authors use mini-batch SGD, with momentum $0.9$, and with the learning rate adjusted based on $\eta_p = \frac{\eta_0}{(1 + \alpha p)^\beta}$, where $p$ indicates the portion of training completed (linear from $0$ to $1$), $\eta_0 = 0.01$, $\alpha = 10$ and $\beta = 0.75$, which was optimized for low error on the source. The MMD and entropy parameters, set as above, were maintained at $\lambda = 0.3$ and $\gamma - 0.3$.

Results

Results from the Office-31 Experiment

Results from the Office-Caltech Experiment

In aggregate, the network outperformed all comparison methods, across all transfer tasks. Broadly speaking the network saw the largest increases in accuracy on the hard transfer tasks (for instance $\mathbf{A} \to \mathbf{C}$), where the source-domain discrepancy is large. The authors take this to mean that the proposed model learns "more adaptive classifiers and transferable features for safer domain adaptation." They further indicate that standard deep learning techniques (i.e. just AlexNet) perform similarly to standard shallow techniques (TCA and GFK). Deep-transfer methods which focus on feature adaptation perform significantly better than the standard methods. The proposed RTN, which adds in additional considerations for classifier adaptation, performs even better.

In addition, the ablation study found a number of interesting results:

1. The RTN (mmd) outperforms DAN, which is founded on a similar method, but contains multiple MMD penalties (one for each layer instead of on a bottleneck), and is as such less computationally efficient;
2. The addition of the entropy penalty [RTN (mmd+ent)] provides significant marginal benefit over the previous RTN (mmd);
3. The full RTN [RTN (mmd+ent+res)] performs the best of all variants, by diminishing returns are seen over the addition of the entropy penalty.

Overall the authors claim that the RTN (mmd+ent+res) is now regarded as state-of-the-art for unsupervised domain adaptation.

Discussion

t-SNE Embeddings Comparing the Performance of DAN and RTN

The Mean and Standard Deviations of the outputs from the Source Classifier, Target Classifier, and Residual Functions. As expected, the residual function provides a small, but non-zero, contribution.

The accuracy of tests by varying the parameter $\gamma$. We first see an increase in accuracy up to an ideal point, before having the accuracy fall again.

The corresponding weights of the classifier layers, if trained on the labeled source and target data, exhibiting the differences which exist between the two classifiers in an ideal state.

Visualizing Predictions (Versus DAN)

DAN uses a similar method for feature adaptation but neglects any attempt at classifier adaptation (i.e. it makes the shared-classifier assumption). In order to demonstrate that this leads to the worse performance, the authors provide images showing the t-SNE embeddings by DAN and RTN on the transfer task $\mathbf{A} \to \mathbf{W}$. t-SNE is a nonlinear dimensionality reduction technique that is particularly well-suited for embedding high-dimensional data into a space of two or three dimensions, which can then be visualized in a scatter plot [18]. The images show that the target categories are not well discriminated by the source classifier, suggesting a violation of the shared-classifier assumption. Conversely, the target classifier for the RTN exhibits better discrimination.

Layer Responses and Classifier Shift

The authors further consider the mean and standard deviation of the outputs of $f_S(x)$, $f_T(x)$ and $\Delta f(x)$ to consider the relative contributions of the different components. As expected, $\Delta f(x)$ provides a small (though non-zero) contribution to the learned source classifier. This provides some merit to the idea of residual learning on the classifiers.

In addition, the authors train classifiers on the source and target data, with labels present, and compare the realized weights. This is used to test how different the ideal weights are on separate classifiers. The results suggest that there is, in fact, a discrepancy between the classifiers, further motivating the use of tactics to avoid the shared-classifier assumption.

Parameter Sensitivity

Lastly, the authors test the sensitivity of these results against the parameter $\gamma$. They run this test on $\mathbf{A}\to\mathbf{W}$ in addition to $\mathbf{C}\to\mathbf{W}$, varying the parameter from $0.01$ to $1.0$. They find that, on both tasks, the increase of the parameter initially improves accuracy, before seeing a drop-off.

Conclusion

This paper presented a novel approach to unsupervised domain adaptation which relaxed assumptions made by previous models with regard to the shared nature of classifiers. Emphasis on this paper is portrayed on unsupervised domain adaptation and on mismatches between the source-target classification results (i.e. the marginal distribution difference of source and target). The proposed deep residual network learns through the perturbation function, which is created through the difference of classifiers. The deep residual network also has the ability to couple the feature learning and feature adaptation to minimize the marginal distribution shift.

Like previous models, this proposed network leverages feature adaptation by matching the distributions of features across the domains. In addition, using a residual network and entropy minimization tactic, the target classifier is allowed to differ from the source classifier by implementing a new residual transfer module as the bridge. In particular, this approach allows for easy integration into existing networks and can be implemented with any standard deep learning software.

For follow-up considerations, the authors propose looking for adaptations which may be useful in the semi-supervised domain adaptation problem.

Critique

While the paper presents a clear approach, which empirically attains great results on the desired tasks, I question the benefit to the residual block that is employed. The results of the ablation study seem to suggest that the majority of the benefits can be derived from using the MMD and Entropy penalties. The residual block appears to add marginal, perhaps insignificant contributions to the outcome. (In practical applications, there is no guarantee that the source classifier and target classifier can be safely shared. So the residual transfer of classifier layers is critical. ) Despite this, the use of MMD loss is not novel, and the entropy loss is less well documented, and less thoroughly explored. Perhaps a different set of ablations would have indicated that the three parts, indeed, are equally effective (and the diminishing returns stems from stacking the three methods), but as it is presented, I question the utility of the final structure versus a less complicated, less novel approach. The authors do not evaluate their results in terms of [math]\displaystyle{ \mathcal{H}\Delta\mathcal{H} }[/math] which defines a discrepancy distance [6] between two distributions [math]\displaystyle{ \mathcal{S} }[/math] (source distribution) and [math]\displaystyle{ \mathcal{T} }[/math] (target distribution) w.r.t. a hypothesis set [math]\displaystyle{ \mathcal{H} }[/math]. Using it, we can obtain a probabilistic bound [19] on the performance εT (h) of some classifier h from T evaluated on the target domain given its performance εS (h) on the source domain.

The same authors have further improved on their methods since the release of the present paper [20]. Their latest approach uses joint adaptation networks. The network processes source and target domain data using CNNs. The joint distributions of these activations are then aligned. The authors claim that this method yields state of the art results with a simpler training procedure.

One thing that authors assume is that the feature map for both source and target distributions are the same, and they just differ in the classifier part. This has to be made more clear.

References

1. https://en.wikipedia.org/wiki/Domain_adaptation
2. https://people.eecs.berkeley.edu/~jhoffman/domainadapt/
3. Glorot, Xavier, Antoine Bordes, and Yoshua Bengio. "Domain adaptation for large-scale sentiment classification: A deep learning approach." Proceedings of the 28th international conference on machine learning (ICML-11). 2011.
4. Tzeng, Eric, et al. "Deep domain confusion: Maximizing for domain invariance." arXiv preprint arXiv:1412.3474 (2014).
5. Long, Mingsheng, et al. "Learning transferable features with deep adaptation networks." International Conference on Machine Learning. 2015.
6. Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." International Conference on Machine Learning. 2015.
7. Tzeng, Eric, et al. "Simultaneous deep transfer across domains and tasks." Proceedings of the IEEE International Conference on Computer Vision. 2015.
8. He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
9. Yang, Jun, Rong Yan, and Alexander G. Hauptmann. "Cross-domain video concept detection using adaptive svms." Proceedings of the 15th ACM international conference on Multimedia. ACM, 2007.
10. Duan, Lixin, et al. "Domain adaptation from multiple sources via auxiliary classifiers." Proceedings of the 26th Annual International Conference on Machine Learning. ACM, 2009.
11. Duan, Lixin, et al. "Visual event recognition in videos by learning from web data." IEEE Transactions on Pattern Analysis and Machine Intelligence 34.9 (2012): 1667-1680.
12. http://vision.stanford.edu/teaching/cs231b_spring1415/slides/alexnet_tugce_kyunghee.pdf
13. https://en.wikipedia.org/wiki/Reproducing_kernel_Hilbert_space
14. Long, Mingsheng, et al. "Learning transferable features with deep adaptation networks." International Conference on Machine Learning. 2015.
15. He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
16. Grandvalet, Yves, and Yoshua Bengio. "Semi-supervised learning by entropy minimization." Advances in neural information processing systems. 2005.
17. More information on residual functions https://www.youtube.com/watch?v=urAp0DibYlY
18. Maaten, Laurens van der, and Geoffrey Hinton. "Visualizing data using t-SNE." Journal of Machine Learning Research 9.Nov (2008): 2579-2605.
19. Ben-David, Shai, Blitzer, John, Crammer, Koby, Kulesza, Alex, Pereira, Fernando, and Vaughan, Jennifer Wortman. A theory of learning from different domains. JMLR, 79, 2010.
20. M. Long, H. Zhu, J. Wang, M. I. Jordan. Deep Transfer Learning with Joint Adaptation Networks. Proceedings of the 34th International Conference on Machine Learning. 2017.

Expert review from the NIPS community can be found in https://media.nips.cc/nipsbooks/nipspapers/paper_files/nips29/reviews/99.html.

Implementation Example: https://github.com/thuml/Xlearn