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

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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
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Reviews:
[{"review_id": "sEZ9Jh7wPG", "reviewer": "Reviewer_Xqv1", "summary": "This paper proposes a new method called Adversarial Reweighting (AR) for partial domain adaptation (PDA) where the target label space is a subset of the source label space. AR aims to estimate the weight of source instances via an adversarial training manner and Wasserstein distance and then exploits the weight to train a simple objective (re-weighted cross-entropy on source + entropy minimization on target). Results on several PDA benchmarks verify the effectiveness of AR.", "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": "Pros:\nDifferent from previous PDA methods relying on class-level weights, the proposed AR method explores Wasserstein distance and learns the instance-level weight for each target sample, which is interesting and new in this field.\nPromising results on several PDA benchmarks, especially 78.3 on the Office-Home dataset.\n\nCons:\nthe results of [BA^3US, ECCV2020] are not fairly compared since a weak variant BAA is compared throughout this paper. It seems that BA^3US performs better for Office and some other tasks like I->C.\nit is also very confusing that the baseline method shown in each table achieves much higher results than previous PDA methods.\nIn figure3, the parameters sound sensitive, how do the authors determine these parameters for an unsupervised domain adaptation task? In fig.3(b), the authors need to compare AR with other PDA methods like BA^3US to study the effectiveness of AR.\n\nminor comments:\nif the Wasserstein distance is replaced by a vanillna JS distance, how AR performs for these PDA tasks?\n\n[BA^3US, ECCV2020] Jian Liang, Yunbo Wang, Dapeng Hu, Ran He, and Jiashi Feng. A balanced and uncertainty-aware approach for partial domain adaptation. In ECCV, 2020.", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "See my comments above.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "6 hours"}, "extra_text": {}}, {"review_id": "rbiNMqBHeWA", "reviewer": "Reviewer_jtrd", "summary": "This paper presents a novel approach for partial domain adaptation (PDA) based on the adversarial reweighting of source samples. The motivation of this paper is quite clear that the source private sample may cause negative transfer in such a partial domain adaptation setting. Reweighting is a common practice to address such challenges. In this paper, the authors propose an improved version that the sample weights can be updated with the help of adversarial learning based on a discriminator. It is a smart design that shows promising results.", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "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": "Strengths:\n+ As mentioned above, the motivation of this paper is quite clear and convincing.\n\n+ The proposed adversarial re-weighting technique is simple but seems effective.\n\n+ The whole paper is organized logically, and the discussion of related works is comprehensive enough to cover most of the literature in this field. \n\n\nWeaknesses:\n- Although the motivation is clear, the whole paper is built upon the well-explored techniques such as sample re-weighting and adversarial training, making me feel like a combination. I am doubtful about the originality of this paper for the standard of NeurIPS.  \n\n- The proposed method seems too simple, and there is no significant improvement (comparing Baseline and AR) on some datasets like DomainNet.\n\nQuestion:\n1. I doubt the fair comparison between the proposed method and other baseline methods. It seems the naïve version (Baseline) outperforms other PDA methods by a large margin.\n\nIn all, I recommend the score 5 due to the weaknesses and questions. I will consider upgrading the score if the authors provide a strong rebuttal.\n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "Yes. The authors provide adequate discussion of limitations and potential negative societal impact.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "4"}, "extra_text": {}}, {"review_id": "NOZXC_Zovy0", "reviewer": "Reviewer_Hqei", "summary": "This paper proposes a partial domain adaptation method, through an adversarial reweighting approach. Instead of learning a domain-invariant feature space, the proposed method reduces the domain shift through a source data re-weighting method.", "questions": "", "limitations": "", "rating": 5, "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": "In general, the paper is well written and easy to follow. The proposed method has some novelty. It identifies some of the limitations of previous PDA methods and proposes a re-weighting model for re-weight the source data to achieve distribution alignment, rather than learning a domain-invariant feature space, to alliterate negative transfer in PDA. However, some of the statements are not very convincing, the novelty is limited, and the experiments are not fair and complete enough. The details can be found in the limitation part.", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "- It claims that previous PDA methods cannot work well on challenging benchmarks, like VisDA-2017 and DomainNet. It would be better to give more details on what aspects are these benchmarks more challenging, and compared to which benchmarks?\n\n- The main cause of mistakenly assigning high weights to the source-only-class data for the previous methods is not very convincing. What are the key insights of the causes? It is good that Section 2 demonstrates the limitations of the previous methods through some experimental findings. However, it lacks more in-depth analysis, and thus the justifications are not convincing enough.\n\n- It claims that previous PDA methods heuristically setting the weights of the source data, which is not precise. For example, the discriminator-based methods [5, 46] also automatically learn the source data weights through adversarial training rather than heuristically setting them.\n\n- How/why does the proposed method can deal with the limitations of previous PDA methods? The proposed method assume that by re-weighting the source domain data, the domain distribution can be mitigated. However, this assumption is very strong. The domain shift between the source and target domains may not be able to be reduced by simply re-weighting the source domain data, especially for the so-called challenging benchmarks with large domain shift, which motivates the subsequent UDA methods that reduce the domain shift by learning a domain-invariant feature space.\n\n- It argues that the proposed adversarial learning-based re-weighting method is different from previous kernel MMD-based methods and optimal transport model. However, to my understanding, they are all very commonly used criteria for aligning different distributions in domain adaptation, the only difference is the chosen metric/divergence for measuring the distribution shift. Thus, the novelty of the re-weighting method is limited.\n\n- The comparisons to the previous method in the experiments part are not fair and complete. First, the author adopts different network architecture compared to previous PDA methods. For example, a state-of-the-art spherical network is adopted for the classifier C. Second, some comparison results are missing. For example, the results on ImageNet-Caltech, DomainNet, and VisDA-17 of some previous PDA methods that perform better than the proposed method on Office-31 are missing, e.g. RTNet[6] and DPDAN[43]. Thus, it is hard to judge the superior of the proposed method over these methods.\n\n- The analysis of the experimental results is not sufficient, especially on why does the proposed method only perform comparably to other PDA methods on Office-31 dataset?\n\n", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "6"}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper points out that the existing partial domain adaptation method has negative transfer when some source-only data are mistakenly assigned high weights. The authors thus propose an adversarial reweighting approach to minimize the distance between reweighted source and target domains. The proposed method outperforms existing methods on several domain adaptation datasets.

However, there are several concerns. 1) The motivation of the paper is not strong enough. The claims in the paper are not convincing. For example, the paper claims that existing PDA methods do not work well on the challenging datasets, such as visDA2017, but it does not explain why such datasets are challenging. Also, the analysis of the drawbacks of existing methods is not thorough enough. For example, the discriminator-based methods [5,46] also automatically learn the source domain weights using adversarial learning rather than heuristically setting them. 2) The proposed adversarial learning method differs from existing methods only in distribution divergence measures. The novelty of the proposed method is limited. 3) The performance of the proposed method does not clearly outperform existing methods by a large margin. Also, the comparison might not be fair because the authors aopt a different network architecture compared to previous PDA methods. The authors have added a long list of new results in the rebuttal, which means the manuscript needs significant modifications of the experimental section.

Overall, the paper will have some practical values if the experimental results are further strengthened. However, given the above concerns, especially those on the novelty of the paper, we think the paper does not meet the acceptance requirements of NeurIPS.

Decision: Accept (Poster)