Towards Stable and comprehensive Domain Alignment: Max-Margin Domain-Adversarial Training
Authors: Jianfei Yang
Han Zou
Yuxun Zhou
Lihua Xie
Venue:  (2020)
Keywords: domain adaptation
transfer learning
adversarial training

Abstract:   Domain adaptation tackles the problem of transferring knowledge from a label-rich source domain to an unlabeled or label-scarce target domain. Recently domain-adversarial training (DAT) has shown promising capacity to learn a domain-invariant feature space by reversing the gradient propagation of a domain classifier. However, DAT is still vulnerable in several aspects including (1) training instability due to the overwhelming discriminative ability of the domain classifier in adversarial training, (2) restrictive feature-level alignment, and (3) lack of interpretability or systematic explanation of the learned feature space. In this paper, we propose a novel Max-margin Domain-Adversarial Training (MDAT) by designing an Adversarial Reconstruction Network (ARN). The proposed MDAT stabilizes the gradient reversing in ARN by replacing the domain classifier with a reconstruction network, and in this manner ARN conducts both feature-level and pixel-level domain alignment without involving extra network structures. Furthermore, ARN demonstrates strong robustness to a wide range of hyper-parameters settings, greatly alleviating the task of model selection. Extensive empirical results validate that our approach outperforms other state-of-the-art domain alignment methods. Additionally, the reconstructed target samples are visualized to interpret the domain-invariant feature space which conforms with our intuition. 

Under review as a conference paper at ICLR 2020
TOWARDS STABLE AND COMPREHENSIVE DOMAIN ALIGNMENT: MAX-MARGIN DOMALN-ADVERSARIAL TRAINING
Anonymous authors Paper under double-bline review
ABSTRACT
Domain adaptation tackles the problem of transferring knowledge from a label- rich source domain to an unlabeled or label-scarce target domain. Recently domain-adwersaaril training (DAT) has shown promising capacity to learn domain- invariant feature space by reversing the gradient propagation of a domain classifier. However, DAT is still vulnerable in several aspects including (1) training instability due to the overwhelming discriminative ability of the domain classifier in adversar- ial training, (2) restrictive feature-level alignment, and (3) lack of interpretability or systematic explanation of the learned feature space. In this paper, we propose a novel Max-margin Domain-Adversariaa Training (MDAT) by designing an Adver- sarial Reconstruction Network (ARN). The proposed MDAT stabilizes the gradient reversing in ARN by replacing the domain classifier with a reconstruction network, and in this manner ARN conducts both feature-level and pixel-level domain align- ment without involving extra network structures. Furthermore, ARN demonstrates strong robustness to wide range of hyper- prpar-paraeeters settings, greatly alleviating the task of model selection. Extensive empirical results validate that our approach outperforms other ofet---rt- domain alignment methods. Additionally, the reconstructed target samples are visualized to interpret the domain-invariant feature space which conforms with our intuition.
INTRODUCTION
Deep neural networks have gained great success on a wide range of tasks such as visual recognition and machine translation (LeCun et al. 2013). They usually require large number of labeled data that can be prohibitively expensive to collect, and even with sufficient supervision their performance can still be poor when being generalized to new environment. The problem of discrepancy between the training and testing data distribution is commonly referred to as domain shift (Shimodairal 2000). To alleviate the effect of such shift, domain adaptation sets out obtain model trained in label-rich source domain to generalize well in an unlabeled target domain. Domain adaptation has benefited various applications in many practical scenarios, including but not limited to object detection under challenging conditions (Chen et al.] 2018). cost-effective learning using only synthetic data to generalize real-world imagery (Vazquez al. 2013). etc.
Prevailing methods for unsupervised domain adaptation (UDA) are mostly based on domain alignment which aims to learn
features by reducing the distribution discrepancy between the source and target domain using some pre-defined metrics such as maximum mean discrepancy (Tzeng al. 2014). Recently, Ganin & Lempitsky (2015) proposed achieve domain alignment by domain- adversarial training (DAT) that reverses the gradients of a domain classifier to maximize domain confusion. Having yielded remarkable performance gain, DAT was employed in many subsequent UDA methods (Long et al. 20188] Shu et 2018). Even so, there still exist three critical issues of DAT that hinder its performance: (1) as the domain classifier has high-capacity to discriminate two domains, the unbalanced adversarial training cannot continuously provide effective gradients, which is usually overcome by manually adjusting the weights of adversarial training according to specific tasks; (2) DAT-based methods cannot deal with pixel-level domain shift (Hoffman et al. 2018); (3) the domain-invariant features learned by DAT are only based on intuition but difficult to interpret, which impedes the investigation of the underlying mechanism of adversarial domain adaptation.

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To overcome the aforementioned difficulties, we propose an innovative DAT approach, namely Max- margin alignment. To demonstrate its effectiveness, we develop Adversarial Reconstruction Network (ARN) that only utilizes MDAT for UDA. Specifically, ARN consists of a shared feature extractor, label predictor, and reconstruction network (i.e. decoder) that serves domain classifier. Supervised learning is conducted on source domain, and MDAT helps learn domain-invarian features. In MDAT, the decoder only focuses on reconstructing samples on source domain and pushing the target domain away from margin, while the feature extractor aims to fool the decoder by learning to reconstruct samples on target domain. In this way, three critical issues can be solved by MDAT: (I) the max- max-margin loss reduces the discriminative capacity of domain classifier, leading to balanced and thus stable adversarial training: (2) without involving new network structures, MDAT achieves both pixel-Ievel and feature-level domain alignment; (3) visualizing the reconstructed samples reveals how the source and target domains are aligned. We evaluate ARN with MDAT on five visual and non-visual UDA benchmarks. It achieves significant improvement to DAT on all tasks with pixel-level or higher-level domain shift. We also observe that it is insensitive to the choices of hyperparameters and as such is favorable for replication in practice. In principle, approach is generic and can be used to enhance any UDA methods that leverage domain alignment as an ingredient.
Training (MDAT), to realize stable and comprehensive domain
2 RELATED WORK
Domain adaptation aims to transfer knowledge from one domain to another. [Bnn-David et al. (0010) provide an upper bound of the test error on the target domain in terms of the source error and the Hdiittiitee As the source error is stationary for fixed model, the goal of most UDA methods
to minimize the H H-distancee by reducing some metries such as Maximum Mean Discrepancy
(MMD) Tzeng et al. 2014: Long et al., 2015) and CORAL (Sun & Saenko 2016). Inspired by
Generative Adversarial Networks (GAN) (Goodfellow et al., 2014). [Ganin Lempitsky (2015)
proposed to learn domain-invarian features by adversarial training, which has inspired many UDA
methods thereafter. Adversarial Discriminative Domain Adaptation (ADDA) tried to fool the label classifier by adversarial training but not in end-to-ent manner. CyCADA (Hoffman et al. 2018) and PixeIDA (Bousmalis et al. 2017) leveraged GAN to conduct both feature-level and pixel domain adaptation, which yields significant improvement yet the network complexity is high.
Another line of approaches that are relevant to our method is the reconstruction network (i.e. the decoder network). The success of taage-to-imagee translation corroborates that it helps learn pixel- level features in an unsupervised manner. In UDA, Ghifary et al. (2016) employed a decoder network for pixel-level adaptation, and Domain Separate Network (DSN) (Bousmalis et al. 2016) further leveraged multiple reconstruction networks to learn domain-specific features. These approaches treat the decoder network as an independent component that irrelevant to domain alignment Glorot et al.) (0 In this paper, our approach proposes to utilize the decoder network as domain classifier MDAT which enables both feature-level and pixel-level domain alignment a stable and straightforward fashion.
3 PROBLEM FORMULATION
3.1 PROBLEM DEFINITION AND NOTATIONS
In unsupervised domain adaptation, we assume that the model works with a labeled dataset Xs and unlabeled dataset Xy. Let X {(x], (xx{,/())e vf)hie( denote the labeled dataset of N, samples from the source domain, and the certain label y² belongs to the label space Y that is a finite set (Y ===1h 1,2,..., K). The other dataset Xr {x (EL.E has N, samples from the target domain but has no labels. We further assume that two domains have different distributions. i.e. x² ~ Ds and x Dr. In other words, there exist some domain shift (Ben-David al., (2100) between Dg and
Dr. The ultimate goal is to learn model that can predict the label v, given the target input x².
3.2 IMBALANCED MINIMAX GAME IN DOMAIN-ADVERSARE TRAINING
To achieve domain alignment,
Training (DAT) is a minimax game between shared feature extractor F for two domains and domain classifier D. The domain classifier is
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Feature embeedings
[=1)
Libel predictor
(gc 0g)
Gy (2* By)
Tiok Loss
Source ibblled data
Max-margin Domain-Adversarial Training
Fara
(x*)
Gx(xx 0,)
Cx; 8.)
MSE Loss
Target cata
Foalura estractor
Reconstmution
(xF)
Figure 1: The proposed architecture is composed of a shared feature extractor Ge for two domains, a label predictor Gy and a reconstruction network Gy. In addition to the basic supervised learning in the source domain, -nvaaiint features. Specifically, the network G, aims to reconstruct the source samples x² and impede the reconstruction of the target samples x², while the extractor G, tries to fool the reconstruction network in order to reconstruct the target samples x².
adversarial reconstruction training enables the extractor Ge to learn
trained determine whether the input sample belongs the source the target domain while the feature extractor learns to deceive the domain classifier, which formulated as:
min max LDAT (D,, (D,, = Ez~D, [In Fxx) + EL [In (1 D(F(x)))
(1)
In DAT, we usually utilize CNN as the feature extractor and fully connected layers (FC) as the domain classifier. DAT reduces the cross-domain discrepancy, achieving significant performance improvement for UDA. Nevertheless, the training of DAT is rather unstable. Without sophisticated tuning of the we observe that such instability is due to the imbalanced minimax game. The binary domain classifier D can easily achieve convergence with very high accuracy at an early training epoch, while it much harder for the feature extractor F to fool the domain classifier and to simultaneously perform well on the source domain. In this sense, the domain classifier dominates DAT, and the only solution is to palliate the training of D by tuning the hyper-parameters according to different tasks. In our method, we restrict the capacity of the domain classifier so as to form a minimax game in a harmonious manner. Inspired by the max-marg max-margin loss Support Vector Machine (SVM) (Cristianini al. 2000) (i.e. hinge loss), if we push the source domain and the target domain away from margin rather than as as possible, then training task of fool D becomes easier. For binary domain classifier, we define the margin loss
DAT cannot reach the convergence. Through empirical experiments,
Lmargin (y) [0, m t y/
where y the predicted domain label, [:] max((0)) (0, m is a positive margin and is the ground truth label for two domains (t = -1 for the source domain and / for the target domain). Then we introduce our MDAT scheme based on an innovative network architecture.
3.3 MAX-MARGIN DOMAIN-ADVERSAN TRAINING
Besides the training instability issue, DAT also suffers from restrictive feature-level alignment - lack of pixel-level alignment. To realize stable and comprehensive domain alignment together, we first propose an Adversarial Reconstruction Network (ARN) and then elaborate MDAT.
As depicted in Figure our model consists of three parts including a shared feature extractor G. for both domains, label predictor Gy and a reconstruction network G,. Let the feature extractor G, (x; 0e) be function parameterized by 0, which maps an input sample x to a deep embedding Let the label predictor (z, fly) be task-specific function parameterized by 8, which maps embedding z to a task-specific prediction ÿ. The reconstruction network G, (z: 0,) is a decoding function parameterized by 0, that maps embedding its corresponding reconstruction &
The first learning objective for the feature extractor G. and label predictor G, is to perform well in the source domain. For a supervised K-way classification problem, is simply achieved by minimizing
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the negative log- ligelikelihood of the ground truth class for each sample:
N
N, Σ yf log (G
Crask
C,(xx
(3)
where y is the one-hot encoding of the class label vf and the logarithm operation is conducted on the softmax predictions of the model.
The second objective is to render the feature learning to be domain-invariant. This is motivated
by the covariate shift assumption (Shimodaira) 2000) that indicates the feature distributions S(z) {Ge (x; 0x) } and [z) {Ge (x; ] Dr} are similar, the source label predictor Gy can achieve a similar high accuracy in the target domain. To this end, we design a decoder network domain classifier, and then MDAT could be applied for stable training. Different from the normal binary domain classifier, MDAT lets the decoder network G, only reconstruct the features in the source domain and push the features in the target domain away from margin m. In this way, the decoder has the functionality of distinguishing the source domain from the target domain. The objective of training Gy. is formulated
G, that serves
N
N +
No (x))+,
min
Lmargin (x:)) min
Σ (x,)
Σ m j=1
(4)
where m is positive margin and (.)- is the mean squared error (MSE) term for the reconstruction loss that is defined
(x) = ||G. (G,,(G(( x1
(5)
where || 112 denotes the squared Lo-norm
Oppositely, to form a minimax game, the feature extractor Ge learns to deceive G, such that the learned target features are indistinguishable the source ones, which is formulated by:
N
min
C. (x)).
(6)
==1
Then the whole learning procedure of ARN with MDAT can be formulated by:
N
C, + Σ (x)),,
(7)
6. 0
==1
min Σ (xf) + Σ -,
(8)
where Ly denotes the negative log-likelihood of the ground truth class for labeled sample (x), and a controls the interaction of the loss terms. In the following section, we provide theoretical justifications how MDAT reduces the distribution discrepancy, and discuss why it is superior to the classic DAT.
3.4 THEORETICAL JUSTIHICATIONS
In this section, we provide the theoretical justifications how the proposed method reduces the distribution discrepancy for UDA. The rationale behind domain alignment is motivated from the learning theory of non-conservative domain adaptation problem by Ben-David al. (Ben-David
et al. 2010):
Theorem 3.1 Let H be the hypothesis space where h E H. Let (Ds, c.) and (Dr, 6)) be the two domains and their corresponding generalization error functions. The expected error for the target domain is upper bounded by
(h) < cx(h)++ + dnan (D) +A H.
where dHAH (Ds, DT) 2 suphh PPanDD (h1 (2) 4 h2)))) Praw Dy (x) & ha(x))) and ming (h) (e))
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Theoretically, when minimize the H.A.H-distance the upper bound of the expected enror for the target domain is reduced accordingly. As derived in DAT (Ganin & Lempitsky 2015), assuming a family of domain classifiers H. to be rich enough to contain the symmetric difference hypothesis set of Hp. such that Ap = [h[h = h1 < h2, h1, h2 E Hp) where @ is XOR-function, the empirical disAaned has upper bound with regard to the optimal domain classifier h:
dH, (Ds, Dr) 2 sup Pr [h(z) = 0] Pr ((()) - 11, z~DD
(10)
hEHA D~
where Ds and Dr denote the distributions of the source and target feature space Zş and Zr, respectively. Note that the MSE of G, plus a ceiling function form of domain classifier lz(z). Le. [[m [-)++ 0.5] for m 1. It maps source samples to 0 and target samples to 1 which is exactly the upper bound in Eq 10 Therefore, our reconstruction network G, maximizes the domain discrepancy with margin and the feature extractor learns to minimize it oppositely.
3.5 DISCUSSIONS
Compared with the conventional DAT-based methods that are usually based on a binary logistic net- work (Ganin & Lempitsky 2015), the proposed ARN with MDAT is more attractive and incorporates new merits conceptually and theoretically:
(1) Stable training and insensitivity to papee-parameters Using the decoder as domain classifier with margin loss restrain its overwhelming capacity in adversarial training, the minimax game can continuously provide effective gradients for training the feature extractor. Moreover, through the experiments in Section we discover that our method shows strong robustness to the hyper- parameters, i.e. a and m, greatly alleviating the parameters tuning for model selection.
(2) Richer information for comprehensive domain alignment. Rather than DAT that uses bit of domain information, MDAT utilizes the reconstruction network as the domain classifier that could capture more domain-specific and pixel-level features during the unsupervised reconstruction (Bousmalis et al. 2016). Therefore, MDAT further helps address pixel-level domain shift apart from the feature-level shift, leading to comprehensive domain alignment straightforward manner.
(3) Feature visualization for method validation. Another key merit of MDAT is that MDAT allows us to visualize the features directly by the reconstruction network. crucial to understand to what extent the features are aligned since this helps to reveal the underlying mechanism of adversarial domain adaptation. We will detail the interpretability of these adapted features Section 4.3
4 EXPERIMENT
In this section, we evaluate the proposed ARN with MDAT on a number of visual and non-viste non-visual UDA tasks with varying degrees of domain shift. We conduct ablation study to corroborate the effectiveness of MDAT and unsupervised reconstruction for UDA. Then the sensitivity of the hyperparameters is investigated, and the adapted features interpreted via the reconstruction network in ARN.
Setup. We evaluate our method on four classic visual UDA datasets and a WiFi-based Gesture Recognition (WGR) dataset (Zou al. 2019). The classic datasets have middle level of domain shift including MNIST (LeCun et al. 1998), USPS (Hull 1994), Street View House Numbers (SVHN) (Netzer et al. 2011) 1) and Syntheuic Digits (SYN). For fair comparison, we follow the same CNN architecture as DANN (Ganin & Lempitsky 2015) while using the inverse of Ge as G, with pooling operation replaced by upsampling. For the penalty term o, we choose 0.02 by searching over the grid [10-2 1). We also obtain the optimal margin
= 5 by search over (10-1, 10). Then we use the same hyperparameter settings for all tasks to show the robustness. For the optimization, we simply use Adam Optimizer (Ir = 2 10 10-4, 81 = 0.5, 32 0.999) and train all experiments for 50 epochs with batch size 128. We implemented our model and conducted all the experiments using the PyTorch framework. More implementation details are illustrated in the appendix.
Baselines. We evaluate the efficacy of our approach by comparing it with existing UDA methods that perform three ways of domain alignment. Specifically, MMD regularization (Long et al. 2015) and Correlation Alignment (Sun & Saenko (2016) employ the statistical distribution matching. DRCN (Ghifary et al. 2016) and DSN (Bousmalis al. 2016) use the reconstruction emror for UDA,
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Source Target
MNIST USPS
USPS MNIST
SVHN MNIST
SYN
SVHN
Source-Onie Only model
78.2
63.4
54.9
86.7
Train on target
96.5
99.4
99.4
91.3
[S] MMD (ong et al.
2015
81.1
71.1
88.0
[S] CORAL (Sun & Saenko
2016
80.7
63.1
85.2
[R] DRCN" (Ghifary
2016
91.8
73.7
82.0
87.5
[R] DSN (Bousmalis ct al
2016
91.3
82.7
91.2
[A] DANN Ganin
2016
85.1
73.0
74.7
90.3
[]] ADDA Zzng ct al
2011
89.4 4
90.1
76.0
[A] CyCADA (Hoilman el al
2018
95.6
96.5
90.4
[A] CADA (Zu et al.
2019)
96.4
97.0
90.9
[A] MECA (Morerio et al.
2018
95.2
90.3
ARN w.o. MDAT
93.1+0.3
76.5+
67.4++0.9
86.8-+0.5
ARN with MDAT (proposed)
0..
98.4+-0.1
97.4++0.3
92.0+-0.2
Table 1:
We compare with general, statistics- (S). reconstruction-basse (R) and adversarial- based (A) state-of-the-art approaches. We repeated each experiment for times and report the average and standard deviation (std) of the test accuracy in the target domain.
training including DANN (Ganin Lempitsky 2015), ADDA (Tzeng et al., 2017), MECA (Morerio et al. 2018), CyCADA (Hofmman ct al. (018)) and CADA (Zou et al. 2019). For all transfer tasks, we follow the same protocol as DANN (Ganin & Lempitsky 2015) that uses official training data split in both domains for training and evaluates the testing data split in the target domain.
while many prevailing UDA methods adopt domain-adversane
4.1 OVERALL RESULTS
USPS. Both datasets are composed of grey-scale handwritten images with diverse stroke weights, leading to low-level domain shift. Since USPS has only 7291 training images, USPS -MISST is more difficult. As shown in Table our method achieves state-of-the-an the-t--rtttttttt accu- racy of 6% on MNIST-USPS and 98.4% on -NNIST, which demonstrates that ARN can tackle low-level domain shift by only using ART (rather than many adversarial UDA methods that adopt other loss terms to adjust classifier boundaries or conduct style transfer).
SVHN-MNIST and SYN-SVHI, The SVHN dataset contains RGB digit images that introduce significant vari- ations such as scale, background, embossing, rotation, slanting and even multiple digits. The SYN data consists of 50A RGB images of varying color, background, blur and orientation. These two tasks have tremendous pixel-level domain shfit. The proposed method achieves a state-of- the-art art performance of 97. 97.4% for SVHN -NISST. far ahead of other DAT-based methods, significantly improv- ing the classic DANN by 22.7%. Similarly, ARN with MDAT also achieves noticeable improvement of 5.3% compared with the source-only model, even outperforming the supervised SVHN accuracy 91.3%.
Table 2: Comparisons on WGR.
Source Target
Room A
Room B
Method
Accuracy (%)
Source-only
58.4+00.7
[S] MMD
61.2++0.5
[R] DRCN
69.3++-033
[A] DANN
68,2+-0.2
[A] ADDA
71.5100.
[A] CADA
88.810.11
ARN+MDAT
91.3110.2
WiFi Gesture Recognition with Distant Domains. To
evaluate the proposed method on a vinuvl UDA task, we applied our method to the WiFi gesture recognition dataset (Zou et al.) 2019). The WiFi data of six gestures regarded as two domains. The results in Table demeontaetttt that our approach significantly improves classification accuracy against Source-Only and DANN by 32.9% and 23.1 1%, respectively.
collected in two rooms

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0.01
0.03
0.07
0.1
0.2
0.3
0.5
1.0
DANN
71.1
74.1
72.7
74.1
74.7
9.6
9.7
10.3
ARN (i 1)
95.7
95.9
93.3
93.2
80.1
75.3
73.1
67.5
m
0.5
0.7
2.0
5.0
10.0
ARN (a = 2e 2
64.5
75.2
90.0
92.6
96.0
97.4
97.7
96.7
Table 3: The accuracy (%) with different hyperparameters on AVHN-MNER
4.2 ABLATION STUDY AND SENSITIVITY ANALYSIS
The contribution of MDAT and image reconstruction in ARN We design ablation study to verify the contribution of MDAT and unsupervised reconstruction in ARN. To this end. we discard the term (x*) Eq/4 and evaluate the method, denoted as ARN w.o. MDAT in Table 1 (1) Comparing ARN w.o. MDAT with source-only model, we can infer the effect of unsupervised reconstruction for UDA. It is observed that ARN w.o. MDAT improves tasks with low-level domain shift such as M which conforms with our discussion that the unsupervised reconstruction is instrumental learning low-level features. (2) Comparing ARN W.O. MDAT with the original ARN, can infer the contribution of MDAT. Table [able[[showe that the MDAT achieves impressive margin- of-immprvement. For -MISS and SVHN- -NISS,, the MDAT improves ARN w.o. MDAT by around 30%. It demonstrates that MDAT which helps learn domain-invarian representations is the main reason for the tremendous improvement.
Parameter sensitivity. We investigate the effect of and m on Table show that ARN achieves good performance as a E [0.01, 0.1] and even with larger a ARN is able achieve convergence. In comparison, denoting cannot converge when a > 0.2. For the sensitivity of m. the accuracy of ARN exceeds 96.0% as m 1. These analyses validate that the training of ARN is not sensitive the parameters and even in the worst cases ARN can achieve convergence.
The results in
the weight of adversarial loss, the DANN
Gradients and training procedure. We draw the training procedure with regard loss and target accuracy in Figure 2(b) and Figure 2(a) respectively. In Figure 2(b) ARN has smoother and more effective gradients (L,) for all a, while the loss of DAT domain classifier (L.a) gets extremely small at the beginning. This observation conforms with our intuition, which demonstrates that by restricting the capacity of domain classifier MDAT provides more effective gradients for training feature extractor, leading to more stable training procedure. This could be further validated in Figure 2(b) where the ARN accuracy is more stable than that of DAT across training epochs.
1.2
1.0
Target Accurary (86
0.8
60
0.6
ARN (a 0.02)
40
0.05)
0.4
c. 0.02)
c, (a 0.05)
ARN (a=00.1
(a- 0.05)
ARN e. 0.10
ARN (a - 1.0)
ARN C, (a- 0.1)
ARN & 1.0)
20
0.02)
0.2
ARN C, fa- 1.0)
c, 0.02)
DAT (a 0.1)
ARN C, (ar 0.02)
C,
DAT la 1.01
10
20
30
40
50
10
20
30
40
50
Epoch
Epoch
(a) Convergence
(b) Test Accuracy
Figure 2: The training procedure with regard to loss and test accuracy. (C,, := Eq. 6 C, the domain loss of DAT (Ganin & Lempitsky 2015); a is the penalty term of Le and L...)
Eq.
4
La
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Source Images
Target Images
R-Target Images
2
0 4 4
0
8
3 2
8
0
6
Z
9
7
3
9 7
6
5
C
9
7 O
2
O 2
7
7
O
USIST-+USPS
3
L.
7
2
2 7 2 9
I
9
0
7
O
0
8
4 9
D
4
7
4
4
2
5
6
8
2
8 0
3 7
7 7
6
4 5
9
7
3
4
9
6
6
5
USPS-
3
5
4
9
3
3
7
7
3
13
1 2
1
7
D
6 0
9
0
5
1
L 18
110
11
2
0 12
3
110
SVHN-MNIST
4
5
115
Z
6
61
40 5
70
6E
3
140
6
75
65
60
31
SYN-SVHN
12
6
8
88
03
10
Table 4:
Visualizing the source image, target images and reconstructed target images (R-Target Images) for four digit adaptation tasks.
4.3 VISUALIZATION AND ANALYSIS
Interpreting MDAT features via reconstructed images. One of the key advantages of ARN is that by visualizing the reconstructed target images can infer how the features are domainninvariant. We reconstruct the MDAT features of the test data and visualize them in Table 4 It is observed that the target features are reconstructed to source- images by the decoder G,, As discussed before, intuitively, MDAT forces the target features to mimic the source features, which conforms with our visualization. Similar to image-to-image to-iaage translation, this indicates that our method conducts implicit feature-to-feature translation that transfers the target features to source-like features, and hence the features become domain-invariant.
T-SNE embeddings. We analyze the performance of domain alignment for DANN (DAT) (Ganin Lempitsky] (0155) and ARN (MDAT) by plotting T-SNE embeddings of the features Z on the task SVHN WTHNN-MNIST In Figure 3(a) the source-only model obtains diverse embeddings for each category but the domains are not aligned. In Figure ((b) the DANN aligns two domains but the
decision boundaries of the classifier vague. In Figure 3 the proposed ARN effectively aligns two domains for all categories and the classifier boundaries are much clearer.
(a) Source-only Model
(b) DANN
(c) ARN
Figure 3: T-SNE visualization on SVHN-MNIST with their corresponding domain labels (red: tar- get; blue: source) and category labels (10 classes) shown the left and right subfigures, respectively.
CONCLUSION
We proposed domain alignment approach namely max-margin domain-adversarril training (MDAT) and a MDAT-based network for unsupervised domain adaptation. The proposed method offers effective and stable gradients for the feature learning via an adversarial game between the feature extractor and the reconstruction network. The theoretical analysis provides justifications on how minimizes the distribution discrepancy. Extensive experiments demonstrate the effectiveness of our method and we further interpret the features by visualization that conforms with our insight. Potential evaluation on semi-supervised learning constitutes our future work.
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Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. Domain-adversarial training of neural networks. The Journal of Machine Learming Research, 17(1)2096-2030, 2016.
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Under review as a conference paper at ICLR 2020
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abs/ abs/1412.3 3474
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Under review as a conference paper at ICLR 2020
APPENDIX
IMPLEMENTATION DETAILS
Hyperparameter For all tasks, we simply the same hyperparameters that chosen from the sensitivity analysis. We use a = 0.02 and m = 5.0, and we reckon that better results can be obtained by tuning the hyperparameters for specific tasks.
Network Architecture For a fair comparison, we follow the network in DANN (Ganin & Lempitsky 2013) for digit adaptation and simply build the reconstruction network by the inverse network of the extractor. Here we draw the network architectures in Table 5 For WiFi gesture recognition, we adopt
the same architecture as CADA (Zu et al. 2019) that modified version of LeNet-5.
Layer Index
Feature Extractor
Decoder Network
Label Predictor
0
32 32 x 3 Image
1
5 5 conv. 64 ReLU
2048 dense, ReLU
10 dense, softmax
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3072 dense, ReLU
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conv. 64 ReLU 5 x conv. 128 ReLU
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max-pool, stride
upsample
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5 x conv. 128 ReLU
5 x 5 conv. 64 ReLU
6
3072 dense, dropout, ReLU
upsample
7
2048 dense, dropout ReLU
5 x conv. 64 ReLU
Table 5: The network architecture used in the experiments.
SENSITIVITY
We have presented all the results of the sensitivity study in Section 4.2 and now we show their detailed training procedures in Figure and 4(b) is observed that the accuracy increases when drops or the margin m increases. The reason is very simple: (1) when is too large, it affects the effect of supervised training on source domain; (2) when the margin m is small, the divergence between source and target domain (i.e. HAH--distncce) cannot be measured well.
Target Accurary (56)
Target Accuraty
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Figure 4: The training procedure of ARN with different hyper-parametent
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Under review as a conference paper at ICLR 2020
VISUALIZATION
Here we provide more visualization of the reconstructed images of target samples. In Figure S the target samples shown in the left column while their corresponding reconstructed samples are shown in the right. We can see that for low-level domain shift such as MNIST++USPS the reconstructed larget samples are very source-like while preserving their original shapes and skeletons. However, for larger domain shift in Figure [(c) digits but simultaneously some noises are removed. Specifically, in Figure 5(d) we can see that one target sample (SVHN) may contain more than one digits that are noises for recognition. After reconstruction, only the right digits are reconstructed. Some target samples may suffer from terrible illumination conditions but their reconstructed digits are very clear, which is amazing.
([c)and and 5(d) they are reconstructed to source-like same
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12


Reviews:
[{"review_id": "HkxaMiKaFB", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This work proposes Adversarial Reconstruction Network (ARN), a network architecture, and Max-margin Domain-Adversarial Training (MDAT), an objective and training procedure for unsupervised domain adaptation. Similar to domain adversarial approaches, the generator aims at finding domain invariant representation while the discriminator now monitors the reconstruction loss of the source and target data using hinge-like lose. The method is very similar to some of the existing works in the literature. Experiment results on the standard digit datasets and the WiFi gesture recognition dataset show that the proposed method outperforms other alternatives.\n\nPros:\n- The writing is good\n- Satisfactory empirical results\n\nCons:\n- The proposed method is very similar to certain methods in the literature\n\nDetail comments:\n(1) The proposed loss function Eq.(8) is very similar to the contrastive loss proposed by Hadsell et al. (2006, Eq.(4)), which is used in Siamese GAN variants (Juefei-Xu et al. 2018, Hsu et al. 2019). Thus essentially the proposed method is an application of an existing GAN technique. Its novelty is limited.\n\n(2) Experiments\n- How are the hyperparameters selected? It is essential to specify the selection criteria when labeled target data is not available.\n- What does the * in DRCN* mean in Table 1?\n- ARN w.o. MDAT may not be the best alternative since the target data is ignored in the reconstruction and the discriminator is not discriminating anymore. A more reasonable alternative would be to ignore the margin and minimize L_r(x^s)-L_r(x^t) to see the effect of the margin.\n\n(3) In Eq.(2), y is said to be the predicted domain label (-1 or +1), which could be not accurate according to the common hinge loss definition.\n\nTypos:\n- In Eq.(1), there is a missing D in the first term. D_s should be \\mathcal{D}_s to match previous notation.\n- In Eq.(2), the \"0,\" is not meaningful given the definition of []^+.\n\nRefs\n- Hadsell, R., Chopra, S. and LeCun, Y., 2006, June. Dimensionality reduction by learning an invariant mapping. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) (Vol. 2, pp. 1735-1742). IEEE.\n- Juefei-Xu, F., Dey, R., Boddeti, V.N. and Savvides, M., 2018. RankGAN: A Maximum Margin Ranking GAN for Generating Faces. In Asian Conference on Computer Vision (pp. 3-18). Springer, Cham.\n- Hsu, C.C., Lin, C.W., Su, W.T. and Cheung, G., 2019. SiGAN: Siamese generative adversarial network for identity-preserving face hallucination. IEEE Transactions on Image Processing, 28(12), pp.6225-6236.\n\n# Update after rebuttal\n\nThank you for the response and additional experiment results. I agree that MDAT and SiGAN are not using the contrastive loss in the same way, but claiming that they are \"totally different\" can be misleading and overstated. It would be better if the paper includes proper discussion about the contrastive loss from the literature and distinguish the particularities between MDAT and SiGAN. Overall, I think the proposed method shows some prosperity thus I have increased my score accordingly.", "extra_scores": {"experience_assessment": "I have published one or two papers in this area.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory."}, "extra_text": {}}, {"review_id": "HJxSQjc9cr", "reviewer": "AnonReviewer4", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "\n###Summary###\nThis paper proposes Max-margin domain adversarial training (MDAT) to tackle the problem of transferring knowledge from a rich-labeled source domain to an unlabeled target domain. This is achieved by designing an adversarial reconstruction network. The proposed MDAT stabilizes the gradient by replacing the domain classifier with a reconstruction network. \n\nThe motivation of the proposed network is based on the observations that the traditional domain-adversarial training is vulnerable in the following aspects:1) the training procedure of the domain discriminator is unstable, 2) it only considers the feature-level alignment, 3) it lacks the interpretable explanation for the learned feature space. \n\nIn the proposed method, the Adversarial Reconstruction Network (ARN) consists of a shared feature extractor, a label predictor, and a reconstruction network. The reconstruction network only focuses on reconstructing samples on the source domain and pushing the target domain away from a margin. The feature extractor tries to confuse the decoder by learning to reconstruct samples on the target domain. \n\nThe paper performs experiments on several domain adaptation tasks on digit datasets. The experimental results demonstrate the effectiveness of the proposed results over several baselines such as DANN, ADDA, CyCADA, CADA, etc. \n\nThe paper also provides empirical analyses such as t-SNE embedding, plotting the loss, etc. to illustrate the effectiveness of the proposed approach. \n\n### Novelty ###\n\nThe model proposed in this paper is extended from the domain adversarial training approach. To stabilize the gradient, the model replaces the domain classifier with a reconstruction network. In this way, the discriminator only discriminates the reconstructed data from the source domain. This idea is interesting and provides some novelty.  \n\n###Clarity###\n\nOverall, the paper is well organized and logically clear. The claims are well-supported by the experiments. The images are well-presented and well-explained by the captions and the text. \n\n###Pros###\n\n1) The paper proposes a Max-margin based approach to tackle domain adaptation. Instead of leveraging the domain discriminator to discriminate the source from the target, this paper utilizes a reconstructor to push the target domain far away from the margin. The idea is interesting and heuristic to the domain adaptation research community. \n2) The experimental results on digit benchmark demonstrate the effectiveness of the proposed method over other baselines including the most state-of-the-art ones. \n\n3) The paper provides many analyses to demonstrate the effectiveness of the proposed method. \n\n###Cons###\n\n\n1) The experimental part of this paper is weak. The paper only provides experimental results on the digit recognition experiments, which is not enough to demonstrate the effectiveness and robustness of the proposed approach. Further experimental results on image recognition or NLP task is desired. \n\nIt will be also interesting to see how does the proposed method perform on large-scale datasets such as DomainNet and Office-Home dataset:\nDomainNet: Moment Matching for Multi-Source Domain Adaptation, ICCV 2019. http://ai.bu.edu/DomainNet/\nOffice-Home: Deep Hashing Network for Unsupervised Domain Adaptation, CVPR 2017. http://hemanthdv.org/OfficeHome-Dataset/\n2) The organization and presentation of this paper should be polished.\n\nBased on the summary, cons, and pros, the current rating I am giving now is weak reject. I would like to discuss the final rating with other reviewers, ACs.\n\n", "extra_scores": {"experience_assessment": "I have published one or two papers in this area.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory.", "review_assessment:_checking_correctness_of_experiments": "I carefully checked the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper."}, "extra_text": {}}, {"review_id": "HJx-7YCVtr", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper proposed a new method for unsupervised domain adaptation. Different from a conventional domain classifier based adaptation, they propose to utilize the loss of autoencoder to extract domain-invariant features. They trained reconstruction network to reconstruct source examples well whereas making reconstruction loss of the target examples large with some margin. Their goal is to stabilize the training of adversarial training for domain adaptation, incorporate pixel-level information, and give interpretable learned feature space. They performed experiments on digits datasets and WiFi Gesture Recognition datasets. Through experiments, they have shown that their method shows better performance than baseline methods and their method is not parameter-sensitive, is stable and provides interpretable adaptation results. \n\nI think their method is interesting and motivation is important. However, their experimental results are not convincing enough. \nFirst, they did not compare their method with recent state-of-the-art methods.  For example, there are classifier's discrepancy based adversarial learning method, Saito, Kuniaki, et al. \"Maximum classifier discrepancy for unsupervised domain adaptation.\". In addition, they did not compare with \"A dirt-t approach to unsupervised domain\nadaptation\", which they cited in the paper. I think their method is for stable and interpretable adversarial learning. So, it does not have to outperform other methods in accuracy. However, they need to show some superiority over these representative adversarial methods. \nSecond, their experiment is only on digits and WIFI datasets. Is the method effective for object recognition datasets, such as Office or OfficeHome? This is an important question to be addressed because the two datasets are benchmark domain adaptation dataset and the behavior on this dataset will show how this method is applicable to various datasets. I would say that the method does not have to outperform state-of-the art methods for these datasets, but they need to show how the method works on this dataset with respect to stability and interpretability. \n\nIn addition, this method seems to have clear connection with \"Zhao, Junbo, Michael Mathieu, and Yann LeCun. \"Energy-based generative adversarial network.\" arXiv preprint arXiv:1609.03126 (2016)\". They need to add this paper to a reference  and explain some connections. \n\nTo sum up, due to the two questions listed above, I think this paper is marginally below the acceptance threshold. Please respond to the questions.\n ", "extra_scores": {"experience_assessment": "I have published in this field for several years.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I carefully checked the derivations and theory.", "review_assessment:_checking_correctness_of_experiments": "I carefully checked the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper."}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper proposes max-margin domain adversarial training with an adversarial reconstruction network that stabilizes the gradient by replacing the domain classifier.

Reviewers and AC think that the method is interesting and motivation is reasonable. Concerns were raised regarding weak experimental results in the diversity of datasets and the comparison to state-of-the-art methods. The paper needs to show how the method works with respect to stability and interpretability. The paper should also clearly relate the contrastive loss for reconstruction to previous work, given that both the loss and the reconstruction idea have been extensively explored for DA. Finally, the theoretical analysis is shallow and the gap between the theory and the algorithm needs to be closed.

Overall this is a borderline paper. Considering the bar of ICLR and limited quota, I recommend rejection.
Decision: Reject