Adversarial Reweighting for Partial Domain Adaptation
Authors: Xiang Gu
Xi Yu
Yan Yang
Jian Sun
Zongben Xu
Venue: NeurIPS 2021 Poster (2021)
Keywords: Partial Domain Adaptation
Adversarial Reweighting
Negative Domain Transfer
Wasserstein

Abstract: Partial domain adaptation (PDA) has gained much attention due to its practical setting. The current PDA methods usually adapt the feature extractor by aligning the target and reweighted source domain distributions. In this paper, we experimentally find that the feature adaptation by the reweighted distribution alignment in some state-of-the-art PDA methods is not robust to the ``noisy'' weights of source domain data, leading to negative domain transfer on some challenging benchmarks. To tackle the challenge of negative domain transfer, we propose a novel Adversarial Reweighting (AR) approach that adversarially learns the weights of source domain data to align the source and target domain distributions, and the transferable deep recognition network is learned on the reweighted source domain data. Based on this idea, we propose a training algorithm that alternately updates the parameters of the network and optimizes the weights of source domain data. Extensive experiments show that our method achieves state-of-the-art results on the benchmarks of ImageNet-Caltech, Office-Home, VisDA-2017, and DomainNet. Ablation studies also confirm the effectiveness of our approach.

Adversarial Reweighting for Partial Domain Adaptation
Xiang Gu, Xi Yu, Yan Yang, Jian Sun, and Zongben Xu
School of Mathematics and Statistics, Xi' an Jiaotong University, P.R. China (xianggu, {jiansun, Hxxxxxxxjji cn
0stuxxxuu. xjjuuedu.... edu.cn
Abstract
Partial domain adaptation (PDA) has gained much attention due to its practical set- ting. The current PDA methods usually adapt the feature extractor by aligning the target and reweighted source domain distributions. In this paper, we experimentally find that the feature adaptation by the reweighted distribution alignment in some state-of-the-art PDA methods is not robust to the "noisy" weights of source domain data, leading to negative domain transfer some challenging benchmarks. To tackle the challenge of negative domain transfer, we propose a novel Adversarial Reweighting (AR) approach that adversarially learns the weights of source domain data to align the source and target domain distributions, and the transferable deep recognition network learned on the reweighted source domain data. Based on this idea, we propose a training algorithm that alternately updates the parameters of the network and optimizes the weights of source domain data. Extensive experi- ments show that our method achieves state-of-the-ate results on the benchmarks of ImageNet- Maateeet-Caltech, Office-Home, VisDA-2011 and DomainNet. Ablation studies also confirm the effectiveness of our approach.
Introduction
Deep learning has achieved impressive success in image recognition 15
However, deep learn- 20 39
ing models often rely on massive labeled training data, requiring an expensive and time-consuming labeling process. To alleviate the dependency of deep learning models on a large number of labeled data, domain adaptation (DA) 27 transfers the knowledge from a relevant source domain with rich labeled data to the target domain. The core of DA is to build a predictive model for the target domain using the training data the source domain, and the model expected to be robust the distribution discrepancy (a.k.a. domain shift) between source and target domains. Domain adaptation methods often train the robust model by aligning distributions of different domains by moment
matching [9] 19 24 28 40 48 or adversarial training [8] 25
38 40 41 42 50) The conventional
closed-set DA methods generally assume that the source and target domains share the same label space. However, this assumption is often not realistic in practice. It is usually difficult to find relevant source domain with identical label space as the target domain. This motivates the research
on the learning problem of partial domain adaptation
49
Partial domain adaptation (PDA) an important subcategory of domain adaptation. PDA tackles the scenario that the label space of the target domain is subset of that of the source domain. Partial domain adaptation is more challenging than vanilla closed-set domain adaptation. Because, besides the challenge of the domain shift, the existence of source-only classes that do not exist in the target domain can cause class-wise feature mismatch when aligning distributions. This potentially leads to negative transfer [27). i.e., the DA approaches hurt the performance of learning in the target
'The corresponding author.
35th Conference on Neural Information Processing Systems (NcurIPS 2021).

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domain. To mitigate the negative transfer, current PDA methods [ 4 21 23 34 46 49 commonly reweight the source domain data decrease the importance of data belonging to the source-only classes. The target and reweighted source domain data are used to train the feature extractor by adversarial training
23
49 or kernel mean matching [21 34 to align distributions.
In this paper, we first propose to measure the hardness of a dataset for PDA using the probability of target domain data being misclassified into source-only classes. We then observe that some of the state-of-the-art reweighted distribution alignment losses, e.g., PADA 4. BAA
[23] and reweighted MMD 34 cause negative transfer on VisDA-2017 32 and DomainNet [31 datasets that are more challenging than Office-31 [36], Office-Home 4S and CmageNet-Caltech [2 33], according to our hardness measure. Specifically, learning/adapting the feature extractor by aligning the feature distributions of the reweighted source and target domain data can even worsen the performance of the baseline model without feature distribution alignment. We find that this negative domain transfer effect is mainly because that these reweighted feature distribution alignment methods are not robust to the "noises" of source data weights, Le., some source-only- data are mistakenly assigned with non-zero weights in the alignment losses.
To tackle the negative domain transfer in PDA, we propose novel adversarial reweighting (AR) approach, which adversarially learns to reweight the source domain data for aligning the distributions of the source and target domains. Specifically, our approach relies on adversarial training to learn the weights of source domain data to minimize the Wasserstein distance between the reweighted source domain and target domain distributions. The weight learning process is conducted in an adversarial reweighting model, using the dual form of the Wasserstein distance. We then define reweighted cross-entropy loss on the reweighted source domain data and use the conditional entropy loss on the target data to train the transferable recognition network for the target domain. The network training and weight learning are performed alternately in an iterative training algorithm.
The current PDA methods [3
49) design/learn source data weights based on the They then train the feature extractor using reweighted distribution alignment loss defined on the target and reweighted source data. Different from them, firstly. we learn the weights of source data in our proposed adversarial reweighting model to decrease the weight of source-only-clase data. Secondly, we reduce the domain gap by reweighting the source domain data, instead of directly optimizing the feature extractor to match feature distributions This strategy may mitigate the negative domain transfer when the source domain data contain "noisy" weights. Note that Balaji et al. [2] and Huang er al. [16] also align data distributions by data reweighing. Huang et al. [16) proposes shallow method that reweights the source data using the density ratio estimated by kernel mean matching for closed-set DA. Differently, we reweight the source data using an adversarial reweighting model for PDA in feature space in deep learning framework. Balaji et al. [2] proposes robust optimal transport model to decrease the importance of the outliers of target data in distribution alignment for closed-set DA. Different from Balaji et al. [2]. we reweight the source domain data to decrease the importance of source-only-class data in the source classification loss to mitigate the negative transfer for PDA.
4 5
21
23 34
46
classifier 3 4 21
23
34
46 or discriminator
5 49
We conduct extensive experiments on five benchmark datasets i.e., Office-31, ImageNet- Office-Homee VisDA-2017, and DomainNet. The results show that our method achieves state-of-the- art results for PDA on ImageNet- Caltech, Office-Home, VisDA-2017. and DomainNet datasets, and achieves competitive results on Office-31. Ablation studies indicate that our method significantly outperforms its baseline on all datasets. Our code is available at htttt:://iithtt com/ XJTUUXJUUUXOO XGU/ Adaptation
Adversarial RiversaarillReweiipee Rwwei Rww.--hteeeeggggnngegggg Por-toor-aal----oeo
In the following sections, we discuss the limitations of feature adaptation by domain distribution aligmment in Sect.2 Section preseets the details of our approach. Section reports the experimental results and Sect. Scconludes this work.
2 Limitations of Feature Adaptation by Domain Distribution Alignment
In this section, we first introduce the setting of PDA and summarize the popular reweighted distribu- tion alignment methods for PDA K 5 21 23 49). We then propose a hardness measure for PDA benchmarks and show the limitations of the reweighted distribution alignment.

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0.4
100
90
wio Align
MND
PADK
75
90
55
0.3
Aos Aacumeyy
UMG po RCT
PADAIN (RD)
45
MAD for RCI
PADA Jur (CD)
70
80
Agge
Al Agge
25 0.0
0.0
0.1
0.2
03
0.4
0.1
0.2
0.3
0.4
OF OH IC VO
OF
0HH 10
V33
INN
Noisa Levll
Nosa Level
(a)
(b)
(c)
(d)
Figure 1: (a) Hardness of five benchmark datasets, i.e. Office-31 31 (OF), Office-Home (OH). ImageNet-
Caltech (IC), VisDA-2017 (VD), and DomainNet (DN), for PDA. (b) Comparison of results of
different reweighted distribution alignment losses (PADA, BAA, and MMD) and the baseline (w/o Align) for PDA on five benchmark datasets. (c-d) Results for reweighted distribution alignment losses with simulated source data weights under varying noise levels in the tasks of (c) Synthetic (S) Real (R) on VisDA-2017 and (d) Clipart (C) + Painting (P) on DomainNet.
Problem setting. In PDA, we are given a labeled dataset S = {xf yf Fi=1 from source domain and an unlabeled dataset T = { j=1 from target domain, where z" and x² E I' respectively denote the source and target data, y E yds is the label of x;. and ) = {0. 1, K} is the source label space. The goal to train a recognition network to predict the label of a/.. Notably, in PDA, the target label space y is a subset of )", i.e.. y C y", which is different from the setting ()' = y* in the closed-set DA. The PDA methods often use feature extractor F' to extract the features and classifier C' to predict labels, and optionally use a discriminator D to discriminate domains.
Summary of reweighted distribution alignment methods in PDA. The challenges of PDA come from two aspects: the domain shift between source and target domains, and the negative effect of source-only-class data in adaptation. To tackle these challenges, most popular PDA methods (e.g., SAN 33 PADA 4 ETN 5.) DRCN (11, BS 233, TSCDA (34,, and IWAN (49) commonly adapt the feature extractor by minimizing the feature distribution distance between the reweighted source domain and the target domain, measured by the reweighted distribution alignment losses. The weights in the reweighted distribution alignment losses usually designed based the output of the classifier or the discriminator. The widely adopted distribution distance metries include the Maximization Mean Discrepancy (MMD) and the Jensen-Shannn the MMD matches the kernel mean embedding of distributions in the Reproducing Kernel Hilbert Space. Minimizing the IS divergence is equivalent to the adversarial training in the Generative Adversarial Network [0). More comparisons of the PDA methods are given in Supp. A.
(JS) divergence. Minimizing
Measuring the hardness of a dataset for PDA. We propose to measure the hardness of dataset for PDA using the probability of the target domain data being misclassified into the source-only classes. Specifically, we first train model by minimizing the source classification loss and the entropy loss on target domain, which is the baseline for PDA. Then the hardness of a PDA task is defined as the average predicted probability of the target domain samples being misclassified into the source-only classes, using the trained model. The average hardness of all tasks in the dataset is taken as the hardness of the dataset. We report the hardness of five PDA benchmark datasets in Fig. [(a) Figure [(a)]inicaaee that VisDA-2017 and DomainNet are more challenging than the other datasets.
Adapting feature extractor by reweighted distribution alignment may lead to negative domain transfer. We show the limitations of the reweighted distribution alignment in this paragraph. We report the average classification accuracies for different reweighted distribution alignment losses (including the reweighted adversarial training losses of PADA 4 and BAA (23) and the reweighted MMD loss in (34)) and the baseline (w/o Align) on five benchmark datasets in Fig. [(b) We also give the detailed results in Supp. B. We can observe in Fig. [ that additionally minimizing these reweighted distribution alignment losses by the feature extractor worsens the performance of the baseline on some challenging datasets, e.g., VisDA-2017 and DomainNet. This indicates that adapting the feature extractor by the reweighted distribution alignment losses leads to negative transfer these datasets. We next investigate the reasons for this finding.
Adapting feature extractor by reweighted distribution alignment is not robust to noisy weights. In the methods of average predicted probability for category of of on the target domain data, i.e the of -th element
23 34), , the weight of source domain data (a², v²) is defined as the
3

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Source images
Reweighted
cross-entropy
Adversarial reweighting
Feature extractor F
Classifier C
model
Conditional
entropy
Target images
Figure 2: Architecture of our Adversarial Reweighting approach for PDA. Red (resp. blue) arrows indicate the computational flow for source (resp. target) domain data. Both source and target images are mapped to feature space by the feature extractor. Our adversarial reweighting model automatically reweights the importance of source domain data match the target domain distribution in feature space to decrease the importance of the data of source-only classes. We define a reweighted cross- entropy loss on the reweighted source domain data distribution and the conditional entropy losses on larget domain data to learn a transferable recognition network for the target domain.
of 1. ΣE= C(F(x)) When the domain gap between source and target domains large, the prediction by the source classifier on the target domain may be uncertain. Then, the predicted probability of classifying the target data source-only classes may be non-zero even possibly significantly larger than zero. Hence, reweighting source classes based on the outputs of the classifier may assign non-zero weights to the source-only classes, i.e., the designed weights may contain "noises". We analyze that the ineffectiveness distribution alignment could be because it is not robust to the "noisy" weights. To illustrate this, we conduct experiments with simulating the source domain data weights with different noise levels. We assign the noise to each source-only class in proportion its weight predicted by the classifier aforementioned. If the weight predicted by the classifier is c', ) for the noise level p [0, 1]. the simulated weight for each source-only class is pc'/ Ekey" ch: for é E ()) Similarly, the simulated weight for each source-sharee class (the class exists in both source and target label spaces) is (1 p)e² / Σxey" c*t for i E )r. If 0, the data of source-only classes with weights of O, and as the p increases, these data are assigned with larger weights, which are taken the "noisy" weights. We also use the simulated weights reweight the importance of source data in the classification loss, denoted as "RC".
adapting the feature extractor by the reweighted
ideally assigned
Figures [(c] and [(d] show the results for different reweighted distribution alignment losses with the simulated weights under varying noise levels. We can see that when the noise level is near zero, all the alignment losses outperform "w/o Align", indicating that the alignment losses lead to positive domain transfer in this case. However, as the noise level increases, the performance of the alignment losses decreases rapidly and even becomes significantly worse than that of "w/o Align". Specifically, in task S-R (Fig. [(c),, when the noise level is 0.1, the performance of the alignment losses begin to be inferior to that of "w/o Align".. Similarly, in task C-P (Fig. l(d), the performance of the alignment losses begin to be inferior to that of "w/o Align" at noise level 0.3. While the "real" noise level (the sum of weights based on the classifier over source-only classes) is larger than 0.3 (resp. 0.4) in the two tasks. Therefore, the negative domain transfer of the reweighted distribution alignment losses may be because of the "noise" in weights. It is also seen that when the noise level ranges from 0 to 0.4, the approach "w/o Align & w/ RC" that reweights data importance in source classification loss consistently outperforms the baseline "w/o Align" in both two tasks. This indicates that reweighting data importance in the source classification loss is more robust to weight noise, compared with the reweighted distribution alignment losses, in the two tasks.
The above observations indicate that adapting the feature extractor by the reweighted distribution alignment is not robust to the noise in source data weights, and can cause negative domain transfer on some challenging datasets. Surprisingly, reweighting data importance the source classification loss may be more robust to weight noise than the reweighted distribution alignment losses.
Adversarial Reweighting for Partial Domain Adaptation
According to the observations in Sect. adapting the feature extractor by the reweighted distribution alignment losses may cause negative domain transfer, when the domain gaps are large. In this work,

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we propose novel adversarial reweighting (AR) approach for PDA, which relies on adversarial learning of source data weights for aligning the distributions of source and target domain features. Figure illustrrate the idea of our approach. We map the source and target data to the feature space by the feature extractor We then design an adversarial reweighting model to reweight the importance of the source domain data in order to match the target domain distribution in the feature space. In this adversarial reweighting model, we reduce the domain gap by reweighting the source domain data features, instead of adapting the feature extractor. We define reweighted cross-entropy loss on the source domain data (with the weights learned by the adversarial reweighting model) and a conditional entropy loss the target domain data to learn a transferable recognition network for the target domain. In the following, we first introduce our network training loss, then discuss the detailed adversarial reweighting model, and finally present our training algorithm.
3.1 Loss for Training Recognition Network
The loss for training the recognition network of our approach is the combination of the reweighted cross-entropy loss on source domain and the conditional entropy loss target domain, defined by
1 Σ (F))) + 1 Σ ((C(F()))0)):0))) (1) ny
C(0r,0c,w w)
T4e
where -(s-) is the cross-entropy loss defined by J(p - (v log Pk. H()) is the conditional entropy defined as (p) = -E Ph log Ph for distribution p (PI. ,P))". (Wi, W2, Wn,)² " is the weights specifying the importance of source domain data such that E Wi n1,, and OF and Oc are respectively the parameters of F and C. The weights are learned in an adversarial reweighting model in Sect. 3.2 Minimizing the reweighted cross-entropy loss enforces the recognition network predict the labels of input images. Minimizing the conditional entropy loss encourages the low-density separation between classes [1] on target domain.
3.2 Adversarial Reweighting Model
Following [5) we assume that the source domain data of shared classes Y" are closer to the target domain data than those source domain data belonging to the source-only classes )" """. To decrease the importance of the source-only-clase data and meanwhile reduce the domain shift, we learn the weights of source domain data by minimizing the Wasserstein distance between the reweighted source domain distribution and target domain distribution. The weight learning process is formulated as an adversarial reweighting model. We first introduce the Wasserstein distance.
Wasserstein distance. The Wasserstein distance is a metric that measures the discrepancy between two distributions. The Wasserstein distance between distributions and v is defined by W W(,, v) min (n [|| yl. where
the set of couplings of μ. and v. i.e., II { (2. y)dy = (x)). It (x. y)dr- = v(y)), ). and ||-I| is the 1o-no Leveraging the Cantorovich-Rubinstenn duality, the Wasserstein distance has the dual form of W(,, = max 1 (fx)) - Exr (f²)),
where the maximization is over all I-Lipschitz functions f Rd RR. Following 14. for computing the Wasserstein distance, we parameterize f by neural network D (discriminator) with parameters 0p. Then, the Wasserstein distance becomes
W (W, v) OnE
max Ex=ulD(a;;0))
ID(x'
where θ = (0D: |D(+; (o); < 1]. We enforce the constraint in Eq. with the gradient penalty technique as in [14]. Equation (2) allows us to approximately compute the Wasserstein distance using gradient-based optimization algorithms the Wasserstein distance in Supp. C. Compared with the other popular statistical distances, e.g., the IS divergence, the Wasserstein distance enjoys better continuity for learning distributions [ [4)
large-scale datasets. We give more details for computing
Adversarial reweighting. Our adversarial reweighting (AR) model defined in the feature space. We denote the extracted feature as of F(x;; 0p) and z/ = F F(az;; Or) for source and target domain data. The empirical distribution of the target domain data T is denoted as Py 1
n, Ej= 8(z5).
where 8(.) the Dirac delta function. The reweighted source domain distribution is denoted as Ps Ps(w) n, E w,d(zf). We then automatically learn the weights in the following principled
5

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model. Based on the aforementioned assumption that the source-only-clase data are more distant from target domain data than the source data of shared classes, we minimize the Wasserstein distance between the reweighted source domain and target domain distributions to learn the weights as follows
min W(Ps (PPs(w))Pr) PT).
wEW
To avoid the mode collapse, i.e., the reweighted distribution is only supported on few data, we enforce W 1)² < p.g Then, the solution space is W = {w w = (W1, W22.... Wn, )", w, 2
W/ n2 -i= (w:- 1)² pme). With the approximation of the dual form in Eq. (
Eq. is transformed to the following adversarial reweighting model:
1 nhe
min max WEW F2g
Σ wi (()
D()):(D) ne
(4)
In Eq. (4) the discriminator is trained to maximize (resp. minimize) the average of its outputs on the source (resp. target) domain to discriminate the source and target domains. Adversarially, the source data weights are learned to minimize the reweighted average of the outputs of the discriminator on the source domain. As a result, the source data (closer to the target domain) with smaller discriminator outputs will be assigned with larger weights. Therefore, defining the reweighted cross-entropy loss on the reweighted source data distribution encourages the transfcrability of the trained recogmition network for the target domain. We will discuss the adversarial training of Eq. (4) in Sect. 3.3
Implementation techniques. To better bridge the domain gaps (experimentally justified in Sect. 4.2 first, we use the spherical logistic regression (SLR) layer 13] as the classifier C. Second, follow- ing 49 the entropy loss in Eq. (I) is only used to update the feature extractor F instead of both F and C. The SLR layer outputs the cosine similarity of the target features and source prototypes [I3 With lower entropy, the learned target features need to be close to the source features so that the classifier (composed of source prototypes) can recognize them more surely. Therefore, our adversarial reweighting and entropy minimization can complement each other to reduce the domain gaps.
Automatically adjusting p. The proper magnitude of is important to our method. If the ratio (D**)/D>* of the sizes of label spaces of target to source domains large, P needs to be small to force more source data to contribute to the reweighted cross-entropy loss, and vice versa. Since the target label space is unknown, we automatically adjust p to enforce the computed Wasserstein distance (the loss value in Eq. ( and is denoted as Aº) in preset interval [Alow] AAP). To do this, we choose an initial value pd of p and a constant e > 1, and adjust p as follows. If AP > A"P (resp. A < Alou),, we adjust p by p cp (resp. p p/c) and resolve Eq. 4 The adjustment is performed till A < AP < AP. We set A = 5.0, A'ow = -5.0, e = 1.2. and p" = 5.0. We show that the performance of our method not sensitive these
in Sect 4.2
3.3 Training Algorithm
To train the recognition network to minimize the loss in Eq. (.. we alternately optimize the network parameters (0p, 0c) and learn the weights w by fixing others as known. We initialize w by w;= = 1
for all i. Then, we alternately run the following two procedures when training the network.
Updating Or and Ic with fixed w. Fixing we update 0g and 0e to minimize the loss in Eq. for M steps, using the mini-batch stochastic gradient descent algorithm.
Updating w with fixed Or and 0c. Fixing OF and fc. we extract the features for all training data on both source and target domains, and learn in Eq. (4). Since Eq. (4) is a min-max optimization problem, we can alternately optimize the weights w and the parameters OD of the discriminator by fixing the other one as known. For reducing the computational cost, we only perform the alternate optimization once, which yields satisfactory performance in experiments. Therefore, we first fix w; 1 for all and optimize 0p to maximize the objective function in Eq. (4) using the gradient penalty technique, as in [14]. Then, fixing the discriminator, we optimize w as follows. We denote d; D(2); 0D) and d [d1, d2, dn.)"" The optimization problem for w becomes
min d'w,
w. s.t. w; 2 0.
(w: - 1)2 < pP
Wi = T2g.
(5)
1=1
Equation (5) is a second-order cone program. We use the CVXPY package to solve Eq. (5).
We
also automatically adjust p as in Sect. 3.2

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Table 1: Accuracy (%) on Office-Home for partial domain adaptation.
Method
Ar- SeCIAr-PrAAA PrAr RRCCI+AACC PPCC
RRw PrrP MAAPr-PCCPPP CCCP Rw-R-wRw--- Rww-Rw--a -wrRw --ii- PPr Avg
ResNet-50 15
46.33
67.51
75.87
59.14
59,94
62.73
58.22
41.79
74.88
67.40
48.18
74.17
61.35
ADDA 2
45.23
68.79
79.21
64.56
60.01
68.29
57.56
38.89
77.45
70.28
45.23
78.32
62.82
CDAN+E 25
47.52
65.91
75.65
57.07
54.12
63,42
59.60
44.30
72.39
66.02
49.91
72.80
60.73
IWAN 39
53.94
54.45
78.12
61.31
47.95
63.32
54.17
52.02
81.28
76.46
56.75
82.90
63.56
SAN 3
44.42
68.68
74.60
67.49
64.99
77.80
59.78
44.72
80.07
72.18
50.21
78.66 65.30
PADA 0
51.95
67.00
78.74
52.16
53.78
59.03
52.61
43.22
78.79
73.73
56.60
77.09 62.06
ETN S
59.24
77.03
79.54
62.92
65.73
75.01
68.29
55.37
84.37
75.72
57.66
84.54
70.45
DRCN 21
54.00
76.40
83,00
62.10
64.50
71.00
70.80
49,80
80.50
77.50
59.10
79,90 69.00
SAPN
58.93
76.25
81.42
70.43
72.97
77.78
72.36
55.34
80.40
75.81
60.42
79.92 71.83
KIN 6
63.20
80.10
80.70
66.70
69.30
77.20
71.60
53,90
84.60
77.40
$7.90
85,50 72.30
BA 23
60.62
83.16
88.39
71.75
72.79
83,40
75.45
61.59
86.53
79.25
62.80
86.05 75.98
DPDAN 46
59.40
79.04
81.79
76.77
58.67
82.18
SHOT 22
64.80
85,20
92.70
76.30
77.60
88.80
79.70
64.30
89.50
80.60
66.40
85.80
79.30
Cls+ Ent (w/ lineur)
54.03
73.61
83.27
69.51
67.56
77.75
69.51
53.73
83.38
74.56
59.34
82.41
70.72
AR (w/ lineur) (ours)
62.13
79.22
89.12
73.92
75.57
84.37
78.42
61.91
87.85
82.19
65.37
85.27
77.11
Cls+Ent
61.61
78.21
86.20
73.19
71.76
79.62
75.11
59.76
86.31
79.16
61.67
83.59
74.68
AR (ours)
67.40
85.32
90.00
77.32
70.59
85.15
78.97
64.78
89.51
80.44
66.21
86.44
78.29
Cls+Ent++AS
63.34
81.12
86.14
74.01
76.53
79.79
77.69
62.57
86.42
78.33
62.69
84.38
76.08
AR+AUS (ours)
68.24
85.60
90.61
75.91
77.54
81.89
81.73
66.39
89.01
83.65
65.61
86.95
79.43
Cls+Ent+LS
62,99
83,59
87,30
74.20
73.05
81.67
79.25
63,46
87.85
78.97
64.54
84.76
76.80
AR+LS (ours)
65.67
87.36
89.62
79.25
75.01
86.97
80.81
65.79
90.61
80.81
65.25
86.12 79.44
We give the pseudo-code of the training algorithm in Supp. D. When the size of source domain dataset is larger than 100k, solving Eq. (5) for all source data at once is infeasible. In such case, we randomly sample a subset (with size N) of the source dataset to update their weights w, and then update Op and Og using them, and iterate these two above procedures.
4 Experiments
We conduct experiments five benchmark datasets evaluate our adversarial reweighting (AR) approach, and compare it with state-of-the-art PDA methods.
Datasets. Office-31 dataset (36 contains 4,652 images of 31 categories, collected from three domains:
Amazon (A), DSLR (D), and Webcam (W). Following [3]. we select images from the 10 categories shared by Office-31 and Caltech-256 12 to build new target domains. ImageNet-Caltedd is built with ImageNet (I) 35 and Caltech- (C) 12), respectively including 1000 and 256 classes. We utilize the 84 shared classes to build the target domain. As most networks are pre- pre-trained on the training set of ImageNet, we use images from ImageNet validation set to build the target domain for task C-+I. Office-Home 43 consists of four domains: Artistic (Ar), Clip Art (CI), Product (Pr), and Real-World (Rw), sharing 65 classes. We use images of the first 25 classes in alphabetical order as the target domain. VisDA-2017 332 is a large-scale challenging dataset, containing two domains: synthetic (S) and real (R), with 12 classes. We use the first 6 classes in alphabetical order as the target domain. DomainNer [3] is another large-scale challenging dataset, composed of six domains with 345 classes. Since the labels of some domains and classes are very noisy, we follow [37] to adopt four domains (Clipart (C), Painting (P), Real (R), and Sketch (S)) with 126 classes. We use the first 40 classes in alphabetical order to build the target domain. On these datasets, we set every domain as the source domain in turn and use each of the rest domain(s) to build the target domain.
Implementation details. We implement our method using Pytorch 30 on a Nvidia Tesla v100 GPU. For the feature extractor F, the last fulle-connected layer. For the discriminator D, we use the same architecture with { (three fully connected layers with 1024, 1024 and nodes), excluding the last sigmoid function. We use the SGD algorithm with momentum 0,9 to update OF and lc. The learning rate of Oc is ten times that of Op. OD updated by the Adam [8] algorithm with learning rate 0.001. Following (8). we adjust the learning rate of Oc by = [T 0.75 where is the training progress linearly changing from O
the ResNet-50 (15) pre-trained ImageNet [35), which excludes
0.01
to 1. We set the batchsize to 36, M 500, and N = 36M. For more details, please see Supp. E.
7

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Table 2: Accuracy (%) on DomainNet for partial domain adaptation.
Method
C-pP
C-~R
C-$
P--
P-R
P-S
R-
R-
R-+
S-C
S-P
S-R
Avg
ResNet-50
41.21
60.01
42.13
54.52
70.80
48.32
63.1
58.63
50.26
45.43
39.3
49.75
51.96
DANN D
27.83
36.64
29.91
31.79
41.98
36.58
47.64
46.81
40.85
25.82
29.54
32.72
35.68
CDAN+E 15
37.46
48.26
46.61
45.50
60.96
52.63
62.01
60.63
54.74
35.37
38.50
43.63
48.86
SAN 3
34.35
51.62
46.23
57.13
70.21
58.25
69.61
67.49
67.88
41.69
41.15
48.44
54.50
PADA
22.49
32.85
29.95
25.71
56.47
30.45
65.28
63.35
54.17
17.45
23.89
26.91
37.41
US 25
42.87
54.72
53.79
64.03
76.39
64.69
79.99
74.31
74.02
50.36
42.69
49.65
60.63
Cls+Ent (w/ lincar)
50.14
64.05
59.81
65.26
76.12
69.50
75.54
69.74
68.55
50.63
54.95
54.44
63.23
AR (w/ lincar) (ours)
56.70
70.36
58.56
65.63
74.80
74.85
75.22
71.17
69.08
53,90
55.70
63.09
65.76
Cls+Ent
49.40
65.69
58.89
65.92
74,82
70.77
75.87
70.72
68.26
50,45
55.70
62.23
64.06
AR (ours)
52.66
68. 24
58.29
66.78
77.53
74.38
76.70
71.77
70.48
53.66
53.60
61.57
65.47
Table 3: Accuracy (%) on Office-31, ImageNet-Caltech, and VisDA-2017 for PDA.
Office-31
ImageNet-Caltech
VisDA-2017
Method
A-D
A-W
D-A
W
W-A
W-D
Avg
C-11
I-C
Avg
R-S S-R
Avg
50
15
83.44
75.59
83,92
96.27
84.97
98.09
87.05
71.29
69.69 70.49
64.28
45.2
54.77
DAN 2
61.78
59.32
74.95
73.90
67.64
90.45
71.34
60.13
71.30 65.72
68.35
47.60
57.98
DANN 9
81.53
73.56
82.78
96.27
86.12
98.73
86.50
67.71
70.80 69.23
73.84
51.01
62.43
IWAN 49
90.45
89.15
95.62
99.32
94.26
99.36
94.69
73.33
78.06 75.70
71.30
48.6 60
59.95
SAN K
94.27
93,90
94.15
99.32
88.73
99.36
94.96
75.26 77.75 76.51
69.70
49.90
59.80
PADA
82.17
86.54
92.69
99.32
95.41
100.0
92.69
70.48 75.03 72.76
76.50
$3.50
65.00
ETN 5
95.03
94.52
96.21
100.0
94.64
100.0
96.73
74.93
83.23 79.08
DRCN 21
86.00
88.50
95.60
100.0
95.80
100.0
94.30
78.90
75.30
77.10
73.20
58.20
65.70
RTNetadd 6
96,20
97.60
92.30
100.0
95.40
100.0
97.20
BA*US 23
99.36 98.98
94.82
100.0
94.99
98.73
97.81
83.35
84.00
83.68
67.56
69.86
68.71
DPDAN 46
96.27
96.82
96.35
100.0
95.62
100.0
97.51
65.26
Cls+Ent (w/ linear)
90.45
87.80
94.68
100.0
94.36
98.09
94.23
77.74
77.82
77.78
69.00
82.32
75.66
AR (w/ linear) (ours)
91.72
97.63
95.62
100.0
95.30
100.0
96.71
81.78
85.83
83.81
74.82
85.30
80.09
Cls+Fnt
80.89
87.12
94.05
94.58
93.95
99.36
91.66
79.60
82.59
81.10
66.63
84.72
75.68
AR (ours)
96.82
93.54
95.51
100.0
96.04
99.67
96.93
82.24
87.12
84.69
78.52
88.75
83.62
4.1 Results
The results on Office-Home and DomainNet are reported in Tables 1 and 2 respectively. The results on Office-31, ImageNet-Catech, and VisDA-2017 are reported in Table 3 Note the results of the compared methods on DomainNet in Table Z are obtained by running their official codes on DomainNet dataset. "CIs+Ent" to denote the baseline approach that minimizes entropy loss on the target domain and source classification loss without using reweighting in Eq. ( Since we used the spherical logistic regression (SLR) layer as the classifier, for completely fair comparisons, report the results of the versions of our AR and Cls+Ent with a linear layer as the classifier (denoted as AR (w/ linear) and Cls+Ent (w/ linear), respectively).
also
On all five datasets, our methods of AR and AR (w/ linear) significantly outperform their baselines of Cls+Ent and Cls+Ent (w/ linear), respectively. On Office-Home, we also combine AR with the adaptive uncertainty suppression (AUS) loss (23 and label smoothing (LSS, which are used in state- of-the-art (SOTA) methods of BAUS (33 and SHOT 22 Our methods of AR+AUS and AR+LS consistently improve the performance of their basclines, respectively. Notably, AR+LS achieves the SOTA result of 79.44% On DomainNet, both AR and AR (w/ linear) significantly outperform the compared methods, and AR (w/ linear) achieves the SOTA result of 65.76%. On ImageNet-Calle Calttech and VisDA-2017 datasets, AR achieves the SOTA results of 84, 44.69% and 83 83.62%, respectively. Note that on Vi11A-2017 and DomainNet datasets, our approach AR (w/ linear) outperforms the previous SOTA method BS (33 with large margin (by 11.88% and 5.13%, respectively).
In Table 3 on Office-31, our approach AR (w/ linear) achieves competitive results compared with other methods. We can see that the accuracies of RTN BA and DPDAN on Office-31 are higher than 97%. This indicates that the prediction of the classifier on the target domain may be reliable and the source weights based on the classifier may contain less noise. In such case,
8

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AR
Classiter
ARR De
Alt
(NE5))
80
Reshie 50
Ol++Ent
AR
0.7
RodMet 50
CiseEnt
80
79
15)
Anarnny
0.6
75
16
25
35
V55 Homo
Methods
Training Steps (x1k)
(a)
(b)
(c)
(d)
(e)
Figure 3: (a) Results for different reweighting strategies. (b) Ablation for MMD and IS-divergence for learning source data weights in our framework on Office-Home. (c) Ablation for learning one weight for each class (OWEC) on Office-Home. (d-e) Accuracy with varying number of target classes
(d), convergence of test errors (c) in task Ar-CI on Office-Home.
positive domain transfer may be achieved by feature adaptation. Note that although RTNetade does not explicitly reweight data using the classifier outputs, the classifier outputs on the target domain utilized as the component of state in reinforcement learning framework for selecting the source data for feature adaptation. Hence, better classifier prediction may be beneficial to performance improvement. On the other four datasets, the average accuracies of compared methods are all lower than 85%, largely lower than the accuracies on Office-31. The prediction of the classifier on the target domain may not be reliable as Office-31, and our method outperforms the compared methods on these datasets. This may be because our proposed approach AR could be more robust to weight noise than other methods.
4.2 Analysis
Comparison with the other reweighting strategies. We compare different reweighting strategies for obtaining the weights in our loss of Eq. (T), including our adversarial reweighting (AR), reweight- ing based on the classifier as in 3 source data in 449 The details of these strategies are given in Supp. A. The results in Fig. (3a) show that our adversarial reweighting significantly outperforms the other two reweighting strategies on 2017 and Office-Home datasets, comfirming the effectiveness of our reweighting strategy.
23], and reweighting by the output of discriminator on
Ablation for MMD and JS-divergence to learn the weights. We conduct ablation studies for JS-divergence and MMD to learn the weights in framework (denoted as AR (w/ JS) and AR (w/ MMD), respectively), on Office-Home dataset. In Fig. 3(b] we can see that our AR using the Wasserstein distance outperforms AR (w/ JS) and AR (w/ MMD). When the supports of source and target distributions are disjoint, the Wasserstein distance may be more suitable to measure their distance than JS-divergence 1. The MMD with widely used kernels may be unable capture very complex distances in high dimensional spaces [ 33 which may make it less effective than the Wasserstein distance in our framework.
Ablation for learning one weight for each class (OWEC). We conduct experiments for learning weight for each class in our framework (denoted AR (w/ OWEC)) Office- Home dataset, as in Fig. ((c) In Fig. [(c] AR with learning the weight for each sample significantly outperforms AR (w/ OWEC) that learns
weight for each class. If the weight is learned for each sample, it is possible to assign higher weights to samples closer to the target domain, even in the same source class. The model trained in this case may be more transferable, because samples (even in the source-shared classes) less relevant to the target domain become less important.
Accuracy with varying number of target classes. We study methods with different numbers of target classes in Fig. 3(d) Our method of AR significantly outperforms Cls+Ent and BA US when the number of classes is smaller than 35. This shows that our method is effective for PDA when the label space mismatch between the source domain and the target domain is larger.
Convergence. In Fig. 3(e) we take the task Ar-CI Office-Home)) as an example to study the convergence of our method. The approach of "ResNet-50" that trains the network using only source data converges fast but has high target test error. Our proposed AR achieves the lowest target emror. We observe that our AR converges slightly slower than Cls+Ent, which may because the source data weights in our reweighted cross-entropy loss updated throughout the training process. We

---

4.0
5.0
8.0
70
ResNec-52 52
PADA
Accursity (%6
Acsuncy (94
88
0.7
CEAN E
BSA
Weight
SAN
AR
Time
0.5
Da
7-
0.04
0.0
0.1
Methods
Class
91011
(a) Sensitivity
(b) Robustness
(c) Cost
(d) Weight
Figure 4: (a) Sensitivity to hyper-parametee in task Ar-CCI. (b) Robustness of AR to weight noise. (c) Computational cost of PDA methods. (d) Average weights for each class in task S->R.
report more empirical evidence, egg, the training loss, for justifying the convergence of the training algorithm in Supp. F.
Sensitivity to hyper-parameterss We testify the sensitivity of our method ryper-parameters A Alow, and p mentioned in Sect. 3.2 as in Fig. 4(a) Figure 4(a) indicates that the performance of our proposed adversarial reweighting is not sensitive to these hyper-parameters.
Robustness of AR to weight noise. To verify the robustness of AR w.rl. weight noise, we conduct simulation experiments for our method under different noise levels. The details of generating the simulated noisy weights are given in Supp. G, due to space limit. From Fig. 4(b) we observe that our AR consistently outperforms the baseline Cls+Ent in tasks C-+P and S S-R under different noise levels. This confirms the robustness of AR to weight noise.
Computational cost. We compare the computational cost of different methods with the total training time in the same training steps (5000 steps), as in [(c) Figure Tigure[Hcc shows that our approach (AR) is comparable to other methods in terms of computational cost.
Feature and weight visualization. We visualize the learned average weights of source domain data for each class in task S-R, in Fig. ((a)
We can see that the source-share--clas
(the first six classes) data get larger weights in general (except the sixth class). We also show the t-SNE em- beddings (26) of learned features in task Ar-:C (Office-Homel in Fig. [ With the conditional entropy minimization, the target features in Fig. [[a] learned by Cls+Ent are aligned with source features of all classes (including the source-only classes), which is unexpected PDA. Fig. (5() proposed AR aligns the target features with partial source fea- tures. These visualizations may partially explain the success of our approach for tackling the problem of PDA. We also visualize more learned features and weights in Supp. H.
(a) Cls+Ent
(b) AR
Figure 5: (a-b) The t-SNE visualization the learned source (blue) and target features (red) by Cls+Ent (a), and AR (b), in task Ar-
Conclusion
In this paper, we experimentally observe that adapting the feature extractor by reweighted distribution alignment is not robust to the "noisy" weights of source domain data and may hurt the performance of learning in target domain. We further propose novel adversarial reweighting approach to tackle the problem of PDA. Extensive experiments show that our proposed approach achieves SOTA results on challenging datasets of MaggeNet-Caltechh office-Home,, VisDA-2017, and DomainNet. our future work, are interested in applying approach the open-set DA 229 and universal DA [47].
10

---

Acknowledgements
This work was supported by NSFC (11690011, U20B2075, 12090021, 11971373, U1811461, 61721002, 12026605) and National Key R&D Program 201 201AA0000220
References
[1] Martin Arjovsky, Soumith Chintala, and Léon Bottou. Wasserstein generative adversarial networks. In /CML, 2017.
[2] Yogesh Balaji, Rama Chellappa, and Soheil Feizi. Robust optimal transport with applications in generative modeling and domain adaptation. In NeurlPS, 2020.
[3] Zhangjie Cao, Mingsheng Long, Jianmin Wang, and Michael I Jordan. Partial transfer learning with selective adversarial networks. In CVPR, 2018.
[4] Zhangjie Cao. Lijia Ma, Mingsheng Long, and Jianmin Wang. Partial adversarial domain adaptation. In ECCV, 2018.
[5] Zhangjie Cao, Kaichao You, Mingsheng Long, Jianmin Wang, and Qiang Yang. Learning to transfer examples for partial domain adaptation. In CVPR, 2019.
[6] Zhihong Chen, Chao Chen, Zhaowei Cheng, Boyuan Jiang, Ke Fang, and Xinyu Jin. Selective transfer with reinforced transfer network for partial domain adaptation. In CVPR, 2020.
[7] Steven Diamond and Stephen Boyd. Cvxpy: A python-embedded modeling language for convex optimization. JMLR, 17(1):2909-2913 291,, 2016.
[8] Yaroslav Ganin and Victor Lempitsky. Unsupervised domain adaptation by backpropagation.
In ICML, 2015.
[9] Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. aavainnadversariall training of neural networks. JMLR,
2016.
[10] Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. arXiv preprint arXiv:1406.266 2014.
[11] Yves Grandvalet and Yoshua Bengio. Semi-supervised learning by entropy minimization. In NeurlPS, 2005.
[12] Gregory Griffin, Alex Holub, and Pietro Perona. Caltech-256 object category dataset. 2007.
[13] Xiang Gu, Jian Sun, and Zongben Xu. Spherical space domain adaptation with robust pseudo- label loss. In CVPR, 2020.
[14] Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville. Improved training of wasserstein gans. In NeurlPS, 2017.
[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, 2016.
[16] Jiayuan Huang, Arthur Gretton, Karsten Borgwardt, Bernhard Schölkopf, and Alex Smola. Correcting sample selection bias by unlabeled data. In NeurlPS, 2006.
[17] Guoliang Kang, Lu Jiang, Yi Yang, and Alexander G Hauptmann. Contrastive adaptation network for unsupervised domain adaptation. In CVPR, 2019.
[18] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint 1rXiv:1412.6980, 2014.
[19] Piotr Koniusz, Yusuf Tas, and Fatih Porikli. Domain adaptation by mixture of alignments of second-or higher-order scatter tensors. In CVPR, 2017.
[20] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep
convolutional neural networks. In NeurIPS, 2012.
[21] Shuang Li, Chi Harold Liu, Qiuxia Lin, Qi Wen, Limin Su, Gao Huang, and Zhengming Ding. Deep residual correction network for partial domain adaptation. IEEE Trans PAMI, In press, 2020.
[22] Jian Liang, Dapeng Hu, and Jiashi Feng. Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation. In ICML, pages 6028-6039, 2020.
[23] Jian Liang, Yunbo Wang, Dapeng Hu, Ran He, and Jiashi Feng. A balanced and uncertainty- aware approach for partial domain adaptation. In ECCV, 2020.
[24] Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan. Learning transferable features with deep adaptation networks. In ICML, 2015.

---

[25] Mingsheng Long, Zhanglie Cao, Jianmin Wang, and Michael I Jordan. Conditional adversarial domain adaptation. In NeurIPS, 2018.
[26] Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. JMLR, 9Nov):2557
2605, 2008.
[27] Sinno Jialin Pan and Qiang Yang. A survey on transfer learning. 22011 2010.
[28] Yingwei Pan, Ting Yao, Yehao Li, Yu Wang. Chong-Was Wah Ngo, and Tao Mei. Transferrable
prototypical networks for unsupervised domain adaptation. In CVPR, 2019.
[29]
Pau Panareda Busto and Juergen Gall. Open set domain adaptation. In ICCV, 2017.
[30]
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary De Vito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, geghpperformanc deep learning library. In NeurlPS, 2019.
[31] Xingchao Peng. Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang. Moment matching for multi-source domain adaptation. In ICCV, 2019.
[32] Xingchao Peng. Ben Usman, Neela Kaushik, Judy Hoffmann Dequan Wang, and Kate Saenko. Visda: The visual domain adaptation challenge. arXiv preprint arXiv:1710.069: 2017.
[33] Sashank Reddi, Aaditya Ramdas, Barnabás Póczos, Aarti Singh, and Larry Wasserman. On the high dimensional power of lincar-time two sample test under mean-shift alternatives, In AISTATS, 2015.
[34] Chuan Xian Ren, Pengfei Ge, Peiyi Yang, and Shuicheng Yan. Learning centtd-domiiissseieee classifier for partial domain adaptation. IEEE TNNLS, 1989):199-2001 2020.
[35] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma. Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. IJC,, 115(3):211-22) 1522, 2015.
[36] Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrel.. Adapting visual category models to new domains. In ECCV, 2010.
[37] Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Trevor Darrell, and Kate Saenko. Semi-supervised domain adaptation via minimax entropy. In ICCV, 2019.
[38] Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku, and Tatsuya Harada. Maximum classifier discrepancy for unsupervised domain adaptation. In CVPR, 2018.
[39] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv: 1556, 2014.
[40] Baochen Sun and Kate Saenko. Deep coral: Correlation alignment for deep domain adaptation.
In ECCV, 2016.
[41] Eric Tzeng, Judy Hoffman, Trevor Darrell, and Kate Saenko. Simultaneous deep transfer across domains and tasks. In ICCV, 2015.
[42] Erie Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell. Adversarial discriminative domain adaptation. In CVPR, 2017.
[43] Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan.
Deep hashing network for unsupervised domain adaptation. In CVPR, 2017.
[44] Cédric Villani. Optimal tramsport: old and new, volume 338. 2008
[45] Ruijia Xu, Guanbin Li, Jihan Yang, and Liang Lin. Larger norm more transferable: An adaptive feature norm approach for unsupervised domain adaptation. In ICCV, 2019.
[46] Junchi Yan, Zhongliang Jing, and Henry Leung. Discriminative partial domain adversarial
network. In ECCV, 2020.
[47] Kaichao You, Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I. Jordan. Universal domain adaptation. In CVPR, 2019.
[48] Werner Zellinger. Thomas Grubinger, Edwin Lughofer, Thomas Natschläger, and Susanne Saminger-Platz. Central moment discrepancy (emd) for domain-invariant representation learn- ing. arXiv preprint /702.0888 1, 2017.
[49] Jing Zhang, Zewei Ding, Wanqing Li, and Philip Ogunbona. Importance weighted adversarial nets for partial domain adaptation. In CVPR, 2018
[50] Yuchen Zhang, Tianle Liu, Mingsheng Long, and Michael Jordan. Bridging theory and algorithm for domain adaptation. In ICML, 2019.
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Reviews:
[{"review_id": "T8txbFge3He", "reviewer": "Reviewer_2HKg", "summary": "The paper introduces the adversarial reweighting (AR) technique for estimating weights in the source domain. Compared to the previous methods on partial domain adaptation, the proposed method seems robust on the effect of negative domain transfer during the adaptation. Authors demonstrate the effectiveness of AR in several partial domain adaptation benchmarks.", "questions": "", "limitations": "", "rating": 7, "confidence": 4, "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": "[Originality] The proposed method, adversarial reweighting, combines reweighting scheme and adversarial learning, which is a brand-new idea, although there have been several methods for estimating weight for the samples.\n\n[Quality] The proposed method is technically sound and plausible. But, the analysis on the learning behavior is not illustrated in the paper. I will give my questions on the limitation section.\n\n[Clarity] There are some curious things in the experiments about state-of-the-art. First, why did you report the performance of BAA [22] instead of BA$^3$US [22] in table 2, 3, and 4. Second, SHOT [R1] is not referred to in table 2, which achieves 78.3 % in Office-Home with partial domain adaptation protocol.\n\n[Significance] As I know, the method is well-designed and the formulation is quite aligned with the partial domain adaptation task. The experiment results are also promising because it consistently improves the baseline over several datasets.\n\n[R1] Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation, ICML 20", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "[Analysis on the proposed method]\nFirst, is there any problem with convergence of the objective in eq 4 and its weight, because adversarial reweighting scheme is implemented on the concept of adversarial learning.\nSecond, could you visualize or plot the learned weights using histograms or etc? Is there evidence related to negative domain transfer?\n\n[Benchmark] As stated in the clarity section, currently the proposed method is not state-of-the-art performance. Can AR be combined with the other state-of-the-art or comparable method rather than used baseline? If it consistently improves the new baseline, this can reinforce the effectiveness of AR.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "8"}, "extra_text": {}}, {"review_id": "Q8ZY5YqZ4Nn", "reviewer": "Reviewer_3wdy", "summary": "The paper aims to solve the challenge of 'noise' in the source domain weight for partial domain adaptation.\n\nThe paper proposes an adversarial reweighting approach to adversarially learn to reweight the source domain data for aligning the distributions of the source and target domains.\n\nThe paper conduct experiments on five benchmark datasets: Office-31, ImageNet-Caltech, Office-Home, VisDA-2017, and DomainNet and show that the proposed method outperforms the prior partial domain adaptation methods. ", "questions": "", "limitations": "", "rating": 5, "confidence": 5, "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": "Strength:\n\nPartial domain adaptation is an important problem setting given the prevalence of large-scale datasets for different applications.\n\nThe paper conduct experiments on mainstream datasets with various domain gaps and sizes.\n\n\nWeakness:\n\nThe paper tries to give an empirical study on why current partial domain adaptation methods on aligning reweighted distribution fail. However, within the empirical study, the authors simulate the situation of noise as all the source-only classes share the same weight. This may not be the case for real datasets. Could the authors do the empirical study based on the realistic datasets and trying to vary the level of noise more realistically?\n\nThe authors only study the influence of the weight noise on the distribution alignment but fail to show the influence of the weight noise on the source classifier. Such an unfair study of weight noise cannot ensure that adding weight on classifier is the best choice.\n\nThe adversarial learning process highly relies on the quality of the feature extractor. You can imagine that if the feature extractor map the input data randomly, then the features z for all the source and target data are random. Then for any weight assignment, the W-distance will be very big. So there is no way to learn the weight assignment. The strong assumption for the proposed method is that in the latent feature space, the distance between the source shared class is closer to the target data than the source only classes. Could the authors explain why the feature space learned by source classification loss satisfy this requirement?\n\nThe paper selects the W-distance as the method to measure the distribution distance but no ablation is done for this choice. There is no guarantee that whether the JS-divergence (adversarial networks) or the MMD distance will perform better. \n\nThe authors adopt one weight for each source data but prior works usually use one weight for each source class because if a class is a source only class, the ground-truth weight for all the data for this class should be 0, which are the same. So we just need to assign one weight to each source class instead of each sample. Assigning one weight to each sample increases the number of parameters.", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "No obvious social impact.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "2"}, "extra_text": {}}, {"review_id": "OXLPXLBoxJ", "reviewer": "Reviewer_KSRV", "summary": "This paper proposed a training algorithm that alternately updates the parameters of network and optimizes the weights of source domain data. A novel Adversarial Reweighting (AR) approach is proposed to adversarially learn the weights of source domain data in order to align the source and target domain distributions.", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "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": "- Figure 1 shows that the different reweighted distribution alignments are not robust to noisy weights, but experiments lack verification of the robustness of the proposed method. \n- Table 1 is not clear, and a clearer caption about domain adaptation setting should be presented.\n- Figure 1 caption S->R and C->P are not clear.\n- The motivation and contributions are not clear.  Introduction and Conclusion both present that the reweighted distribution alignment is not robust to the “noisy” weights of source domain data and may hurt the performance of learning in target domain. But there are no discussions and experiments about robustness of the proposed method.\n- There are some typos. \n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "Adversarial training may bring more calculations, and adversarial learning may be instability during training, it will be better if there are extra discussions about these points. ", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "Four hours"}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper tackles the problem of partial domain adaptation. After considering the reviews, author rebuttal, and discussions the reviewers remained split on their recommendations with one accept, one borderline accept, and one borderline reject. 2HKg found that the work contributed a “brand-new idea” combining re-weighting and adversarial learning and KSRV increased their recommendation from borderline reject to borderline accept post-rebuttal citing that the authors had “given enough results to demonstrate the effectiveness of the proposed model.” The AC has reviewed the weaknesses brought up by 3wdy and determined that the responses and additional experiments authors provided in the rebuttal are sufficient. The authors have in general provided many additional experiments and clarifying thoughts during the rebuttal and need to be sure to include these in the final paper.
Decision: Accept (Poster)