Partial Label Unsupervised Domain Adaptation with Class-Prototype Alignment
Authors: Yan Yan
Yuhong Guo
Venue: ICLR 2023 poster (2023)
Keywords: Partial label learning
label noise
domain adaptation

Abstract: Partial label learning (PLL) tackles the problem where each instance is associated with a set of candidate labels, only one of which is the ground-truth label. Most existing PLL approaches assume that both the training and test sets share an identical data distribution. However, this assumption does not hold in many real-world scenarios where the training and test data come from different distributions. In this paper, we formalize this learning scenario as a new problem called partial label unsupervised domain adaptation (PLUDA). To address this challenging PLUDA problem, we propose a novel Prototype Alignment based PLUDA method named PAPLUDA, which dynamically refines the pseudo-labels of instances from both the source and target domains by consulting the outputs of a teacher-student model in a moving-average manner, and bridges the cross-domain discrepancy through inter-domain class-prototype alignment. In addition, a teacher-student model based contrastive regularization is deployed to enhance prediction stability and hence improve the class-prototypes in both domains for PLUDA. Comprehensive experimental results demonstrate that PAPLUDA achieves state-of-the-art performance on the widely used benchmark datasets.

Published as a conference paper at ICLR 2023
PARTIAL LABEL UNSUPERVISED DOMAIN ADAPTA- TION WITH CLASS-PROTOTYPE ALIGNMENT
Yan Yan', Yuhong Guo
CIFAR Al Chair, Amii, Canada yanyane cunet. carietan. ca, yuhong guo8carleton. ca
CCalleton University, Ottawa, Canada
ABSTRACT
Partial label learning (PLL) tackles the problem where each instance associ- ated with a set of candidate labels, only one of which is the ground-truth label. Most existing PLL approaches assume that both the training and test sets share an identical data distribution. However, this assumption does not hold in many real- world scenarios where the training and test data come from different distributions. In this paper, we formalize this learning scenario new problem called par- tial label unsupervised domain adaptation (PLUDA). To address this challenging PLUDA problem. we propose novel Prototype Aligmment based PLUDA method named PAPLUDA, which dynamically refines the pseudo-labels of instances from both the source and target domains by consulting the outputs of teacher-studen model in moving-average manner, and bridges the cross-domain discrepancy through inter-domain class-prototype alignment. In addition, a teacher-student model based contrastive regularization is deployed to enhance prediction stability and hence improve the class-prototypes both domains for PLUDA. Compre- hensive experimental results demonstrate that PAPLUDA achieves late-of-the-arr performance on the widely used benchmark datasets.
INTRODUCTION
Partial label learning (PLL) is typical weakly supervised learning problem, where each training instance is assigned a candidate label set, only one of which is valid. PL.. has gained increasing attention from the research community due to effectiveness in reducing annotation costs in var- ious real-world scenarios, such as face naming (Hüllermeter & Beringer 2006), web mining (Luo) & Orabonal 2010), and ecoinformatics (Liu & Dietterich 2014). Nevertheless, standard PLL as- sumes the training and test data are sampled from the same distribution. With this assumption, model learned from the training data is expected to generalize well on the test data. However, this assumption does not hold in many real-world scenarios where the training and test data come from different distributions-e.
the training and test data are collected from different sources, or we have an outdated training set due to the fact that data always change over time. In such cases, there would be a discrepancy between the training and test data distributions, and hence naively adopting the off-the- off-the-shelf PLL models can lead to significant test performance degradation. Meanwhile, the unavailability of the ground-truth labels prevents the deployment of existing unsupervised domain
adaptation (UDA) methods (Tzeng et al., 2017 Dong et al. 2021 Na et al. 202T: Shen al. 2022)
We formalize this new learning scenario of PLL with training-test distribution gaps as partial label unsupervised domain adaptation (PLUDA) problem. By integrating the challenges of both PLL and UDA problems, the PLUDA problem has the following characteristics: (1) the source and target domains have different distributions but share the same set of classes; (2) data in the source domain have only partial labels-cace instance is associated with a candidate label set, while the target domain only has unlabeled data; (3) the candidate label set for each source instance can contain both the ground-truth and irrelevant noisy labels, while labels outside of the candidate set true negative labels. The goal of the PLUDA task is to learn a invariant prediction model from the partial-labe source domain that can generalize well in the unlabeled target domain.
Although both PLL and UDA have been studied intensively in the literature, to the best of our knowledge, there is research yet address the integrated challenges of PLUDA in unified

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framework. PLUDA is related to but still distinct from the weakly supervised domain adaptation (WSDA) problem studied in the recent literature (Shu et al. [019 Xie et a 2022). WSDA assumes the ground-truth labels of the source domain instances corrupted (c.g., replaced) with noisy labels, and has been studied as an effort of reducing annotation costs. Some researchers address the WSDA problem based off-the-shelf technologies and obtain good performance. For example, Xie et al. (2022) exploit the bilateral relationships between the source and target domains to construct universal framework, GearNet, based on the existing domain adaptation methods of TCL (Shu al. 2019) and DANN (Tzeng et al. 2017).
In this paper, we propose a novel prototype alignment based partial label unsupervised domain adaptation approach, PAPLUDA, to address the combined PLL and UDA problems simultancously in newly formalized PLUDA learning scenario. The proposed PAPLUDA approach contains three pseudo-label based components that collaborate with each other to tackle PLUDA learning. First, we conduct soft label disambiguation to dynamically rectify the pseudo-labels of training instances from both domains toward the ground-truth labels, which aims to disambiguate the partial labels and set up the foundation for cross-domain class alignment. Second, we propose an inter-domain class- prototype based alignment minimize the discrepancy between the same class prototypes from the source and target domains while maximizing the gaps between the prototypes of different classes. Finally, we deploy a teacher-student model based contrastive regularization enhance the reliable pseudo-labels, and hence improve the class-prototypes and the inter-domain prototype alignment. Overall, the contributions of this paper can be summarized as follows:
A new challenging learming problem. PLUDA. is proposed, which is more practical than separate PLL and UDA problems by simultaneously dropping the common supervised learning assumptions of accurate data labels and identical training and testing distributions.
A novel PAPLUDA approach is proposed to tackle the PLUDA problem.
Comprehensive experiments are conducted on benchmarks and the results validate the ef- fectiveness of the proposed PAPLUDA.
2 RELATED WORK
2.1 PARTIAL LABEL LEARNING
Partial label learning a prevalent weakly supervised learning problem Zhou 2018), where each training instance is associated with set of candidate labels, only one of which is valid. Many meth- ods adapt existing learning techniques to the partial-label training data and disambiguate the can- didate noisy labels by aggregating predictions. For maximum likelihood techniques, the likelihood of each partial label training instance is produced by consulting the probability of each candidate label associated with the training instance (Jin & Ghahramani] 2002 Liu & Dietterich] 2012). For k-nearest neighbor techniques, the candidate labels from neighbor instances are integrated for final prediction in weighted voting manner (Hüllermeier & Beringer (006 Gong et al. 2017 Zhang &
Yu 2015). For maximum margin techniques, the classification margin on each partial label instance defined by discriminating the modeling outputs from candidate labels and non-candidate labels (gguyen & Caruanal 20088 Yu & Zhang, 2016). Apart from adapting the off-the-shelf techniques to partial-label data, some researchers propose to address PLL by adapting the partial-label data to existing learning techniques. For example, Zhang et al. (2017) propose to transform a partial-label training set into multiple binary training sets which can then be used to built multiple binary classi- fiers corresponding to the ECOC coding matrix. WWu & Zhang (2018) adopt a one-vs-one decompo- sition strategy to enable binary decomposition for learning from partial- label data. Although these works produce competitive performance, they are restricted to linear models and have difficulty to handle large-scale datasets.
To alleviate the limitations of the standard PL. methods, deep learning based PLL has recently started gaining attention from the research community. Yao et al. (2020) attempt to address PLL with deep convolutional neural networks by exploiting temporai-ensembling technique. Mean- while, Yan & Guo (2020) handle PLL with multilayer perceptrons by means of batch label cor- rection. Wen et al. (2021) present group of loss functions called leveraged weighed loss, which takes the work of (Ly et al. (2020) as its special case. [eng et al.] (2020) present two provably consistent methods from the perspective of partial label generation: risk-consistent method and
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classffierconssiseent consistent method. Xu et al. 2021) adopt variational inference along network training to progressively refine the latent label distributions, assuming an instance-dependent partial label generation process. In addition, Wang al. (2022) propose class prototype based label disam- biguation method through contrastive learning. which achieves impressive performance on several image classification benchmarks. Although theses deep learning methods have produced notable progress on PLL, the study however is still limited to the supervised learning scenario where both training and test datasets share an identical data distribution.
2.2 UNSUPERVISED DOMAIN ADAPTATION
Unsupervised domain adaptation (UDA), which aims to transfer knowledge learned from label-rich source domain to an unlabeled target domain, has gained tremendous attention from the research community (Hoffman al. 2018, Ghafoorian et al., 2017 Kamnitsas et al.) 2017 Wang & Zheng 2015 Fang et al. 2020) Dong et al. (021)). The main challenge of UDA is to bridge the cross- domain distribution gap and induce transferable prediction models. Some previous research works have attempted to minimize the inter-domain gap based on standard distribution discrepancy criteria, such as the maximum mean discrepancy (Pan al., 2010), Kullback-Leibler divergence (Zhuang et al., 2 central moment discrepancy (Zellinger et al., 2017), and Wasserstein distance (Lee & Raginsky 2018). In addition, several studies attempt to minimize the domaín discrepancy by exploiting a domain discriminator in adversarial learning manner, including domain adversarial network (Ganin et al. (2166) and adversarial discriminative domain adaptation (Tzeng et al. 2017). Recently, some researchers consider enhancing the domain adaptation procedure by exploiting the unlabeled data from the target domain, and propose new UDA approaches based on self-training models (Zou et al. 2019 Chen al. 2020b).
These standard UDA methods however all assume the training data in the source domain are anno- tated with accurate true labels, which are difficult to provide in real-world scenarios. More recently, weakly supervised domain adaptation (WSDA), which addresses both the UDA problem and the noisy label learning issue, has gained increasing attention in reducing annotation demands for high- quality labels (Shu et al., [019 Tzeng et al. 2017 Liu et al. 2019). For example, TCL (Shu et al.) 2019) proposes to train a domain adaptation network by exploiting the selected clean and trans- ferable source instances. GearNet (Xie et al., (022) proposes universal framework based on the off-the-shelf powerful domain adaptation methods, TCL. (Shu et al. 2019) and DANN (Tzeng et al.) 2017), and exploits the bilateral relationships between the source and farget domains to enhance the WSDA training procedure. In this work, we relax the label requirement in the source domain by considering partial labels, which can be easily obtained with crowdsourcing. This new PLUDA problem cannot be directly addressed by existing PLL or UDA methods.
3 PROPOSED APPROACH
We assume a source domain Ds = (x(,, L. with n, partial-label instances and a target do- main DT = [x{) Ri with n, unlabeled instances, where x and x² denote the i-th instance from the source domain Dg and the target domain Dr respectively, and ył {0, 1) denotes the label indicator vector associated with x². I. denotes the number of classes. The multiple values in y? denote either the true label or irrelevant label noise indistinguishably, which forms the candidate label set S; C {1, L} for x We use B, = {(x;, y²)} } m. and B, = [x{] me |
to denote a mini-batch of instances sampled from Ds and Dr, respectively. The task of PLUDA is induce multi-class prediction model from Ds and Dr that can generalize well in the target domain.
We propose to address this novel task of PLUDA by simultaneously performing domain adaptation and partial label disambiguation with inter-domain class-prototyy alignment and teacher-studen model based soft label disambiguation. The proposed PAPLUDA model is illustrated in Figure I The model architecture includes a shared feature extractor G. a teacher-student prediction network (student network F' and teacher network F*), and a feature projection network H. Towards the ob- jective of PAPLUDA, three loss components are designed on the model architecture: (1) classifica- tion loss with soft label disambiguation, which trains a teacher-student model from the partial-labe data in the source domain by performing moving average based soft label disambiguation; (2) inter- domain class- pros--prootype alignment loss, which aims to bridge the information sharing by performing contrastive alignment between the prototypes of the same class
distribution gap for
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Pasudo Label
Predittion
F
+ (1
X)
Uodating
Partiely Labeled Source Dare
etion
Updating
F
Augmented Source Data
Embedd ng
Protokypa
F
Updwling
Augmented Target
Linter
Prediction
F
G
Embeddingg
Pretohype
H
Undat's
Aupporr
Unisbeled Targer Data
Peeudo Label Refirmmnt
G
F'
Undating
Updating
F
Poeudo Label
Figure 1: The proposed PAPLUDA model, which mainly has three loss components: classification loss with soft label disambiguation L.; inter-domain class-prototype alignment loss Linteri teacher- student model based contrastive regularization loss Freg (Lieg and Le All the components mutually benefit from each other to improve the PLUDA learning cooperatively.
in the two domains; and (3) teacher-student model based contrastive regularization loss, which en- hances prediction stability and consequently improves class-prototypes and inter-domain prototype alignment for PLUDA learning. We elaborate these loss components and the proposed approach in the following subsections.
3.1 CLASSIFICATION WITH SOFT LABEL DISAMBIGUATION
As the true labels are hidden within each candidate label set in the source training data, it critical to perform label disambiguation -that separate the true labels from the noisy labels-for inducing good classification model. We deploy teacher-student model as the classification model due to its robustness against noisy labels for stable prediction (Li et al. 2019). We assume the teacher and the student share the same feature extractor G but maintain two different prediction networks: student network and teacher network F'. F and F' have identical structures but different parameters, and they produce multi-class softmax probability prediction vectors. The parameters of the student network, OF, will be learned from the training data, while the parameters of the teacher network, Pp,, will be updated as an exponential moving average (EMA) of the student network parameters (Tavaainnn & Valpola 2017), such as:
= + 1)
(1)
where 7 (0, 1) is weight hyperparameter. Importantly, we propose the teacher network that is typically more robust than the student network to perform label disambiguation:
Siy I[j arg max F' (G(x))) jees,
where II is an indicator function, and J is the disambiguated one-hot label indicator vector for instance x using the teacher network. Such disambiguated one-hot label vectors for the source training instances however would certainly contain mistakes. Directly using them for model training could cause oscillations by largely deviating from the original candidate labels. Hence we propose to perform soft label disambiguation by updating the candidate label distribution vectors with the one-hot vectors (f?) } in moving average fashion:
p Api + (1 -
(3)
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where À (0,1) is hyperparameter, and p denotes the soft disambiguated label vector for the source instance x We initialize P as the original candidate label distribution vector for xf. such as Pi [3:1 T E S.].
Benefiting from the moving average update, we expect this soft label disambiguation procedure can steadily and progressively approach the underlying true label vectors through the iterative training steps. With the disambiguated soft label vectors, we can update the student network F and the feature extractor G by minimizing following classification loss for each instance:
(x). - E j=1 PPjj log F)(((x())))
(4)
where j denotes the indices of the class labels, and (.) denotes the j-th output entry of the student prediction network F, which indicates the predicted probability of the given instance belonging to the j-th class. By iteratively performing soft label disambiguation and prediction model update, we expect the true labels can gradually and statistically identified from the source training data, and hence induce good classification model.
3.2 INTER-DOMAIN CLASS-PROTOTIE ALIGNMENT
The goal of PLUDA is to induce a classification model that generalizes well in the target domain. Hence it is important take the unlabeled target data into consideration and bridge the cross- domain feature distribution discrepancy. In particular, we propose to discriminatively bridge the cross-domain divergence and match the two domains specifically for the given classification task by performing prediction-awan inter-domain class-prototy alignment.
paass-potottppe defined as the mean embedding vector of the instances belonging to the class. Following self-supervised learning (Chen et al.) 2020a). we compute the class-prototypes in a low- dimensional embedding space produced by a projection network H on the extracted features. As the true labels are unknown in the source and target domains, propose to calculate the class- prototypes based on the pseudo-labels predicted by the teacher-student model. Specifically, the class-protts protottes for each training batch Ba computed follows:
c ExtEBs H(G(x²) [[[k: arg max Piel
(5)
ExjEB, I arg max P
where d E t) is domain indicator; y S in the source domain and )" = 21, L} in the target domain. The discrete labudss the source domain are produced from the disam- biguated soft label vectors {pí} computed in Eq. 3.. For the unlabeled target domain, propose to maintain soft label probability vectors {pt} during training in the following way:
p = = + (1 (G(x))
(6)
To alleviate the negative impact and limitation of computing prototypes using the partial-label data in a local batch, we maintain dynamic dictionary C = [cf ... . c'jj in the source domain to store the plass-protoyypes from the previous batch for prototype calibration. After com-
puting the class-prototypes using (5 for the current source batch, we further update them by consulting the dynamic dictionary moving average manner:
cf = (1 >))"
where E [0, 1] is trade-off parameter. This allows us to take the prototype information from previous batches into account, overcoming the potential random bias in a local batch.
With the class-prototypes computed in both domains, we propose to perform inter-domain prototype alignment using the following contrastive loss for each class k:
a(c cf)
ck Cl (ct, ) -log
D(c ct) E MD + k] (R(c), + e
where 12(a, b) = exp(cosine( b)/T) measures the similarity of the given pair, and 7 is the temper- ature hyperparameter. This contrastive loss uses the inter-domain class-prototype pair from the same
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class as the positive sample and the pairs from different classes the negative samples. We expect this contrastive loss can push the inter-domain prototype pair the same class closer to each other and push the inter-domain pair from different classes apart in the embedded feature space, aiming to induce good feature representation space, which the two domains can be discriminatively aligned for classification model training.
3.3 TEACHER- TRACHER-STUDENT MODEL. BASED CONTRASTIVE REGULARIIZATION
To exploit the consistent statistical knowledge learned along model training across both domains and avoid prediction perturbations caused by the noisy label information, we propose a self-supervised contrastive loss in the output space of the prediction networks and calculate the loss in both domains as a regularizer to enhance the stability of the prediction model.
The supervsd contrastive loss aims push the prediction outputs for the variants of the same instance to be similar and for different instances to be dissimilar. It can conveniently exploit the unlabeled target instances. Specifically, for each training instance x,, we first generate an augmented version of it as & Axg(x,), where Aug(-) denotes the data augmentation function (Cubuk et al., 2020
t3) can be computed as: Then the contrastive loss for each instance x in batch Ba (d {s,
1((2 x
Free (x,, (x,,x)
x = llog
Exjen h(x 2 Ex Exjen, 1
where h(a,b) b) label vectors on the given input instance pair using the student network and teacher network respec- tively, and is the temperature hyperparameter. By deploying this contrastive loss regularizer on both domains, we have the following batch-wise total regularization loss:
cossineFF(Giiii F' /((())))) denotes the similarity between the predicted
Creg = Exp Lie (x), x + E Ex Chés (x', x))
(10)
We expect this prediction consistency regularization loss can help induce similar feature representa- tions across domains regarding the classification task and improve the inter-domain class- prost-prot alignment to facilitate domain adaptation.
3.4 OVERALL TRAINING LOss
Finally, by integrating the classification loss in Eq.(4) the inter-domain class-prototype alignment loss in (8) and the teacher-student model based contrastive regularization loss in Eq. (0)) together, we have the following overall batch-wiss wise training loss:
C + a Σ, Ck Clattr + 3Lreg
(11)
where and are trade-off hyperparameters that balance the weights of the inter-domain class- prototype alignment loss and the teacher-student model based contrastive regularization loss respec- tively. The training algorithm is presented Algorithm T in Appendix.
4 EXPERIMENTS
To validate the efficacy of the proposed PAPLUDA, we conduct experiments on two domain adap- tation benchmark datasets by generating partial-label data in the source domain with different noise levels. We report our experimental setting and results in this section.
4.1 EXPERIMENT SETTING
Datasets We conduct experiments on two widely used domain adaptation datasets: Office-31 (Sennoo et al. 2010) and Office-Home (Venkateswara al. 2017). Office-31 is a classical dataset consisting of 4,652 images from 31 classes, which distributed across the following three domains: Amazon (A), Webcam (W), and DSLR (D). These domains contain images collected from ama- zon.com, web camera, and digital SLR camera, respectively. Six different domain adaptation tasks can be constructed from this dataset, each of which one domain the source domain and

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Table 1: Test accuracy (mean:tstd, %) comparison on the partial- Office-31 dataset with label ambiguity level q 0.2. The best result in each column is highlighted in bold.
Tasks
A- W
D
W WD
D D- A
D- W
Average
DANN
11.43+1.71
55.20=1.01
44.28:0.72
72.08=1.13
38.10-1.41
64.09=0.45
54,20=1.077
TCL
61.72.10.88
68.54111.11
47.58.10.36
42.15 1..66.1..05
58 08.4610.61
18,92
GearNetco-teach
33.20.11.97
39.3711.228
38.9910.68
63.37.1.08
33.80_1.51
1..2
43.50.11.56
GEarNeSDANN
62.76+0.48
64.79++.00
46.37+1119
78.88+1622
39.71-1.02
67.08-0.81
1.09
GearNetrrLL
63.55+00882
69,85:+103
49.24:11.23
73.13=0.91
43.86-0.99
60.01=0..44 60.44=0.66
PAPLUDA(OurS)
79.36.10.46
78.72
53.34.10.87
94.37
50.07 0.56
87.11 0.86
73.82.1..66
Table 2: Test accuracy (tea %) comparison the partial-label Office-31 dataset with label ambiguity level q = 0.4. The best result in each column is highlighted in bold.
Tasks
A W
A
W - A
D
-
-
Average
DANN
1..36+t.59
40.01+1.28
35.62:11.6
57.08=1.62
30.61=1.92
52.73=1.81
42.73+1.64
TCL
49.08.1..59
61.4511.88
39.95.1.93
61.25.1.79
35.61-1.87
55.1112.57
50.40.11.77
GearNetco-teach.
24.52+1.18
28.67+0.88
22.69+1.08
45.21+1.36
21.80-1.54
1..8
1..2
GeaNNettANN
1..52.11.63
1..93:11991
38.45
1.86
33.38
1.38
1.55
GearNetren
53,51+0.81
64.79+1.03
41.05+1.93
63.87=1.91
16.17-1.44
56.90=1.60
1,.71+1.45
PAPLUDA(Ours)
76.62.11.06
77.1511011
51.25-10.98
91.5611.66
45.28
1.34
70.71
uses another one as the target domain. Office-Home a challenging domain adaptation benchmark dataset consisting of 15,500 images from 65 classes across the following four domains: Artistic (Ar), Clip Art (CI), Product (Pr), and Real-World (Rw). These domains represent four different image styles, including artistic depictions, clipart images, images without background, and photos taken with cameras respectively. There are twelve different domain adaptation tasks constructed from Offce- each of which uses a different ordered pair of domains as the source and tar- get domains. For the PLUDA experiments, we produce partial labels in each source domain of the two datasets following the partial label generation process in (Lv al. 2020). Specifically, each irrelevant label for an image is uniformly selected as a candidate label with a probability q. which controls the label ambiguity level of the produced dataset. When encountering a special case where no irrelevant label is chosen for an image, we randomly add an irrelevant label to the candidate label set to ensure the whole training set is corrupted thoroughly. For each comparison experiment, we report the average test accuracy and standard deviation based on five independent runs.
Comparison Methods We compare the proposed PAPLUDA approach with three state-of- the-art weakly supervised domain adaptation methods CeearNetco-teaching GearNetDANN, and GearNetrer.) and two baseline methods (DANN and TCL). Each method is configured with the sug- gested parameters according to the respective literature. DANN (Tzeng et al., 2017) designs an ad- versarial domain adaptation mechanism for neural networks to reduce the cross domss- divergence. TCL (Shu et al. 2019) devises a transferable curriculum to select instances for model training, which addresses weakly supervised domain adaptation through self-paced learning. GearNetco-teachinn (Xie et al. 2022) exploits the bilateral relationship between source and target domains for model training and integrates the off-the-shed co-teaching framework (Han et al.) 2018) to improve the model's 's robustness against noisy labels. GearNetpANN (Xie et al. (022) and GearNetrer. (Xie) al. 2022) two universal frameworks for exploiting knowledge from both the source and target domains. They incorporate the effective domain adaptation methods, DANN and TCL, as their core components respectively to address the weakly supervised domain adaptation problem.
Implementation Details To ensure a fair comparison, we adopt the same backbone network and optimizer for all the comparison methods. We use ResNet50 as the backbone network for feature extraction. The projection network is two-layer perceptron that outputs 228-dimensional embed- dings. The prediction network is a two-layer perceptron followed by softmax activation function. The coefficient hyperparameter 17 for the teacher-student model and the hyperparameter y for class- prototype update are set to 0.999 and 0.99, respectively. For the soft label disambiguation, we linearly ramp down À from 0.95 to 0.8. The temperature parameter 7 for similarity calculation is set to 5. We employ a standard stochastic gradient descent optimizer with momentum of 0.9, a weight decay of 0.0005, an initial learning rate of 0.01, and a cosine learning rate decay. The mini-batch size is set to 64. The trade-off parameters and / are chosen from the set {0.1, 0.3, 0.5, 0.7, 0.8. 1} for the two datasets according to the validation performance of the transfer task A + W on the Office-31 dataset and the Ar -> CI task on the Office-Home dataset respectively.
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Table 3: Test accuracy (st, %) comparison on the partial-label Office-Honme dataset with label ambiguity level q= 0.2. The hest result in each row is highlighted in bold.
Tasks
DANN
TCL
GearNelco- teach.
GearNelDANN
GearNetreL
PAPLUDA(Ours)
Ar + CI
17.03+1.16
21.89+1.35
16.47+0.62
18.18+1.10
22.44-0.81
34.14-0.87
Ar + Pr
2..66+1.17
1.345+1.37
25.33-+0.76
33.33++1.17
33.
46.78-t0.97
Ar + Rw
38.2842206
45.59:10.75
18.19:20.72
12.14:t1.37
1.681=1.62
15.68-11.33
CI + Ar 22.66
31.71+1.58
18.22++0.73
27.42+40.78
33.49=1.58
40.76=:0.94
CI + Pr
1,.69+1.25
34.57+1.29
28.80+0.86
33.08++1.16
35.19+1.06
1.224++126
CI + Rw
31.09+1445
40.57+1.43
27.21+0.88
37.52+0.64
42.34+0.93
52.35
Pr - Ar
22,46+0.61
33.87+0062
1..1
26.88+0.91
34.33++
43.25
Pr + CI
1..25+1.83
13.98:+1.83
14.28+
18.87
2..11=1.44
39.60=1.122
Pr + Rw
1,02
50.43++0.87
38.2440.79
42.53++1.08
54.41=1.22
65.31=1.055
Rw + Ar 31.17+1122
46.33+1101
26.29++1.05
35.88+00772
17.45+1.56
57.34--0.74
Rw + CI
21.32+0.84
28.58+1889
18.76+1.20
22.25+1.011
30.97 1.46
43.22++1144
Rw + Pr
1..96+1.52
17.7411.22
18.68-+1.23
52.97+1.35
62.54=1.21
7.130=11.13
Average
28.6511.224
37.21
26.94
32.3911.04
39.17:1.27
50,00 1.02
4.2
COMPARISON RESULTS
4.2.1 RESULTS ON OFFICE-31
The comparison results in terms of test accuracy (mean-sstandard deviation) in the target domain for the six domain adaptation tasks from the Office-31 dataset are reported in Table [ and Table 2 with different label ambiguity levels. Table reports the comparison results with label ambiguity level indicated by = 0.2. From the table, we can observe that the proposed PAPLUDA not only produces the best results on all the six domain adaptation tasks, but also outperforms the comparison methods with substantial performance gains. For example, the proposed approach outperforms the best comparison methods by 20.03%, 15.81 %, 15.49%, and 8.87% the domain adaptation tasks of D + W, A + W, W - D, and A + D, respectively. Such superior performance demonstrates the effectiveness of the proposed PAPLUDA approach in addressing the PLUDA problem.
Table 2 reports the comparison results in a more challenging setting with a higher label ambiguity level of q = 0.4. We can see the proposed approach again consistently outperforms all the other methods in this setting. Moreover, the performance gains achieved by the proposed approach over the other comparison methods are very remarkable. For example, PAPLUDA outperforms the best comparison
comparison methods by 25.52% 23.11% 25.31 %, and 12.36% on the domain adaptation tasks of D W.A A W. W - D, and - D respectively, and it yields an average performance gain of 18% over the best comparison method, GearNetTcL. Compared with the experimental results reported in Table the performance of each comparison method reported in Table in general degrades due to the higher noise level (i.e., label ambiguity level). However, the performance gains achieved by the proposed method over the best comparison methods improve as the noise level increases from the
q 0.2 q 0.4. In particular, the average performance gain achieved by PAPLUDA best comparison method, GearNetreL. increases from 13.81 % to 18% when increases from 0.2 to 0.4. These results demonstrate the effectiveness of the proposed PAPLUDA method in handling high level label ambiguities.
In summary, the experimental results on the Office-31 dataset with different noise levels validate the effectiveness of the proposed PAPLUDA method in dealing with the challenging PLUDA problem.
4.2.2 RESULTS ON OFFICE- OFFICE-HOME
The comparison results on the twelve domain adaptation tasks constructed from the Office-Home dataset are reported in Table From the table, we can observe the follows: (1) Compared with all the other five domain adaptation methods, the proposed approach achieves superior performance across all the twelve tasks. Moreover, its performance gains are very notable. For example, the proposed approach outperforms the best comparison methods by 14.49%, 14.05% and 12.79% on the tasks of Pr CI, CI Pr, and Ar - Pr, respectively. (2) Out of the total 60 comparison cases over 5 comparison methods and 12 domain adaption tasks, the proposed approach outperforms all the comparison methods consistently, which is very remarkable given the fact that different comparison methods have different strengths on various classification tasks. These results on the Office-Home dataset again validate the efficacy of the proposed method in addressing the PLUDA problem.
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Table 4: Results (%) of the ablation study on multiple tasks.
Ablation Variant
D + (q 0.2)
D
(g 01.)
Ar
Pr (q 0.2)
Rw
(g =0.2
Full Model
50.07
45.28
46.78
72.30
M-w/o-Cime
42.40
36.29
44.60
69.53
A-wlo-Lrep
38.42
37.41
45.32
71.12
M-w/o-soft
29.87
17.65
21.17
39.92
M-wlo-F'
47.02
37.02
44.93
71.18
M-wlo-targer
34.28
32.70
40.52
65.30
CLS-source
19.55
10.33
13.22
32.88
4.3 ABLATION STUDY
The proposed approach has three main components: soft label disambiguation, inter-domain class- prototype alignment, and teacher-student model based contrastive regularization. To investigate the impact of these components on addressing the PLUDA problem, compare the proposed full model with the following ablation variants: (1) M-w/o-Limes which drops the inter-domain class-prote prototype alignment loss from the full model by setting
conduct an ablation study to
(2) M-w/o- freg, which drops the contrastive regularization loss by setting 3 = O; (3) M-w/o-soft, which drops the moving average based soft label disambiiguttion/udaae by setting drops the teacher network F' and uses the student network F instead; (5) M-woo-target, which drops the target domain from training by setting a = 0 and removing the regularization loss Cig on the target domain; and (6) CLS-source, which is a baseline that only uses standard classification loss in the source domain by taking the given candidate labels as the prediction targets.
O; (4) M-w/o-F', which
We conduct the ablation experiments several domain adaptation tasks selected from the two datasets. The comparison results are reported in Table 4 Compared to the full model, we can that all the ablation variants have notable performance degradation in different degrees. M- w/o-Lintrr has performance drops of over 7.5% on the two D-> A tasks with different noise lev- els and over 2.1% the Ar-+P and Rw-Pr tasks. These performance degradations suggest the inter-domain glass-prototype alignment loss is important for the proposed method in bridging the cross-domain discrepancy. M-w/o-Lreg also demonstrates similar performance degradations, which validates the contribution of the contrastive regularization loss to the proposed approach. It is worth noting that the variant M-w/o-soft produces substantial performance degradation across all the tasks, with performance drops between 20.2% and 32.38%. These results suggest the proposed soft label critical for handling the label noise PLUDA. The performance gap between the full model and the variant M-w/o-F" suggests that the teacher model is useful for the soft la- bel update and the contrastive regularization and hence contributes to the proposed model. By dropping the target domain from training. M-w/o-targe warrtt produces inferior performance than the M- w/o-Lineer variant. This demonstrates the importance of exploiting the target domain data for domain adaptation, and further validates the contribution of the inter-domain class-prototype alignment loss and the teacher-student model based contrastive regularization loss. Moreover, all the five ablation variants mentioned above-M-wwoo--nee. M-w/o-Lneg M-w/o-soft, M-w/o-F', and M-w/o-target- outperform the ablation baseline CLS-source that tackles neither the PLL challenge nor the UDA challenge. Overall, this ablation study demonstrates all the proposed components are essential for the proposed PAPLUDA approach, which effectively integrates these parts into a coherent model for tackling the entangled PLL and UDA challenges in the PLUDA problem.
disambiguation
5 CONCLUSION
In this paper. we proposed a new challenging problem called partial label unsupervised domain adaptation (PLUDA), which addresses the entangled PL. and UDA challenges simultaneously. We proposed a novel prototype alignment based PLUDA approach called PAPLUDA. It conducts soft label disambiguation in both the source and target domains and minimizes the cross-domain discrep- ancy by performing inter-domain class-prototype alignment. In addition, a teacher-student model based contrastive regularization is deployed to enhance prediction stability and improve the class- prototypes in both domains, thus consequently helping the inter-domain dlassproototppe alignment to facilitate domain adaptation. Extensive experimental results demonstrate the proposed PAPLUDA achieves state-of-fe of-the-art performance benchmark datasets.

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A APPENDIX
A.I TRAINING ALGORITHM
The training algorithm for the proposed PAPLUDA approach is presented Algorithm
Algorithm Trriiiig Algorithm of PAPLUDA
Input: Ds: partial-label source dataset; Dr: unlabeled target dataset; initial PAPLUDA model: F, F', G, H; a, ::: trade-off hyperparameters.
One epoch training:
1: for iter= 1: iterations do
2: Sample mini-batch of samples B, ((x m. from Dy and
i=1
sample mini-batee of samples B, {xx] i=1 from Dr.
3: Perform soft label disambiguation on Bg according to Eq.
4: Compute classification loss Le via Eq. 4 by taking the disambiguated soft labels as target.
5:
Perform soft label disambiguation//upaate on B₁ according to Eq.
6:
Compute and update the class-pree pronsstppst according to Eq.(5) and Eq
7:
Compute the inter-domain class-proton prototype alignment loss Linter via Eq.
8:
Compute the teacher-student model based contrastive regularization loss Zreg via Eq. (TO).
9: Update {F, G, H to minimize the batch-wise training loss in Eq.
Update the teacher model Fx according to Eq.(1.
11: end for
A.2 PARAMETER SENSITIVITY ANALYSIS
90p
classification accuracy
classification accuracy
85
80-
80
75
75
65
0.1
0.3
0.5
0.7
0.8
65
0.3
0.5
0.7
0.8
(a) Varying the value
(b) Varying the value
Figure 2: Test results of the proposed PAPLUDA approach with different and B values the PLUDA task A ( from Office-31.
We investigate the impact of the trade-off parameters and 3 on the performance of the proposed approach by conducting experiments values from 10.1, 0.3, 0.5, 0.7, 0.8, 1}. We first set = 0.5 while varying the a value, and then set 0.7 while varying the 3 value. Note that larger value gives greater emphasis on the inter- domain class-prototype alignment loss, and larger 3 value emphasizes more on the contrastive regularization loss. The test results are presented in Figure 2 We can see that when very small, the performance is relatively poor. As increases, the performance of the approach improves, indicating that the inter-domain class-prototype alignment loss is helpful. However, when a is too large (>0.7), the performance degrades as the inter-domain class- class-prototype alignment loss gradually dominates. This reasonable since the inter-domain class prosotpr alignment loss designed to help the model training rather than dominate the learning procedure. The results for different values demonstrate a similar pattern, which indicates the contrastive regularization loss is a useful auxiliary loss for the proposed approach.
the PLUDA task A W (g=003 0.2) with different a and
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PAPLUDA
GearNetrc.
GearNetpwN
GearNetco teaching
TCL
DANN
100
80
60
0.2
Test results of PAPLUDA with different label noise levels (q values) the W D task from the Office-31 dataset.
Figure 3:
Test accuracy (tt) comparison on the partial-label Office-Homee dataset with label ambiguity level q 0.1. The best result in each row highlighted in bold.
Table 5:
Tasks
DANN
TCL
GearNetco-tend tench..
GearNetDANN
GearNetreL.
PAPLUDA(Ours)
22.04+1.27
1..66+1.477
20.64=0.52
1.167
26.63++0.22
37.06+00.95
32.99=1.17
38.5411.31
31.20=0.74
38.77
10.87=1.111
15.33:11.08
Ar + Rw
41.3112.17
51.75
0.78
45.34
13.86.1
1.14
CI Ar 28.58+0.71
33.21+1.64
22.50=0.86
33.92=0.81
36.62+1.66
44.00=00788
CI - Pr
29.3321.17
36.70:1.449
33.38-0.77
35.21
39.27=1.110
53.14.11.11
CI - Rw
41.50+1.49
11.40+1.37
36.31=1.11
4..98+0.63
44.18+
55.46++0779
Pr + Ar 28.42=0.55
35.31=00.74
24.88=1.37
34.01=0.96
3..85++1443
45.51=
Pr > CI
20.9311.84
25.62.11.88
17.67-1.28
22.8611.17
26.98.1..51
40.6111.12
+ Rw
47.59+1.18
54.38+0.91
44.12-0.85
53.33+
18.15+
67.40+1.226
Rw +
39.04=1.15
48.88411.25
34.67=1.08
42.13-0.82
49.6421.53
58.39:10.86
Rw
25.97110.98
31.47.11.92
20.39-1.26
27.76.1.17
34.22.11.41
1..18
Rw
Pr
54.85+1.69
59.26+11223
56.18=1.28
19.51
6..55+1.27
72.74+1121
Average
34.38+1.28
40.16+1.37
31.90=0.99
38.71+1.11
42.48+1.33
53.11+1.03
A.3 IMPACT OF LABEL. NOISE LEVEI.
We also conduct experiments with different label noise levels (i.c., multiple label ambiguity levels values) on the domain adaptation task of + D. The comparison results are presented in Figure From the figure, comparison methods largely degrades, Nevertheless, the proposed PAPLUDA consistently outper- forms all the other comparison methods even the challenging case with large noise level = 0.7, while the other comparison methods produce very poor performance with high noise lev- els. Moreover, the proposed PAPLUDA maintains large performance gains over the other methods across the range of noise levels. This study further demonstrates the effectiveness of PAPLUDA.
that with the increase of the noise level q, the performance of all the
A.4 ADDITIONAL EXPERIMENTAL RESULTS
To further validate the effectiveness of the proposed approach, we present additional experimental results Office-Home with small noise level & 0.1 in Table From the table, the proposed method produces the best results across all the tasks. Comparing the results in Table and Table we see that with smaller value, the performance of all the comparison methods improves in general, but the proposed PAPLUDA nevertheless maintains notable performance gains the other methods in both settings.
that
14


Reviews:
[{"review_id": "o7q5ipidaq", "reviewer": "Reviewer_uDYu", "summary": "", "questions": "", "limitations": "", "rating": 6, "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": "", "summary_of_the_paper": "This paper proposes a novel problem by combining the settings of PLL and UDA. To tackle this challenging setting, they also propose a novel approach named PAPLUDA which consists of three loss components: classification loss with soft label disambiguation; inter-domain class-prototype alignment loss, and teacher-student contrastive regularization loss. To demonstrate the effectiveness of their proposed method, they perform experiments on two synthesized datasets which are generated from two popular DA datasets, Office-31 and Office-Home. In all experiments, PAPLUDA outperforms baselines by a significant margin. In addition, ablation studies are conducted to verify the importance of each component in their method and its sensitivity to different choices of hyperparameters.", "summary_of_the_review": "Overall the contributions of this paper are two-fold: a novel problem and an efficient solution. The efficiency of their method is backed by experimental results and detailed ablation studies. ", "strength_and_weaknesses": "## Strong points:\n* The motivation for their newly introduced problem is clear and reasonable.\n* The Related work is well-written which helps to position this paper well in the related literature.\n* The authors also did a good job of elaborating on their proposals and providing the intuition behind each component.\n* Their method, PAPLUDA, shows a clear advantage over comparison methods in the new setting.\n* The ablation studies are sufficient to further strengthen their claims.\n\n## Weak points:\n* For the method part, In Section 3, a lot of claims are made without an appropriate reference. For instance, “We deploy a teacher-student model as the classification model due to its robustness against noisy labels for stable prediction.” Why is this claim true? In addition, it is unclear whether some techniques are newly introduced in this paper or have already existed before. For example, the teacher-student models’ design, moving average soft label, class-prototype learning with moving average pseudo-label, and dynamic dictionary, to name a few. Although this is an empirical paper, the authors’ claims still need to be backed by appropriate references to prior works.\n* For the experiment part, while PAPLUDA illustrates strong performance on datasets with high ambiguity, there is a tendency that it cannot outperform baselines when q is small and it approaches 0.\n\n## Questions:\n* Is gamma in (3) and (6) the same parameter?\n* How hyperparameters (coefficients for moving average)  are chosen in Section 4.3? Why are some of them fixed and one is adaptive?\n\n## Minor comments:\n* There are some typos and grammatical errors. For example: “they are restricted to the liner model”, “no one irrelevant label is chosen, we randomly pick a irrelevant label”.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "HY1QxQSARn", "reviewer": "Reviewer_TPqJ", "summary": "", "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": "", "summary_of_the_paper": "In this paper, the authors formalize a new problem setting, called partial label unsupervised domain adaptation (PLUDA), and propose its solution that jointly conducts partial label learning via pseudo-labeling and domain adaptation by class-prototype alignment. ", "summary_of_the_review": "The problem setting tackled in this paper is new and interesting. However, I have several concerns on the design of the proposed solution and its evaluation. I vote for \"weak reject.\"\n\n---\n\nAfter rebuttal, I updated my score from 5 to 6.", "strength_and_weaknesses": "\n<Strength>\n\n- The new problem setting PLUDA proposed in this paper is interesting and should be practically important in some ML applications.\n- The pseudo-label refinement and class-prototype alignment are well designed. Their design seems simple but reasonable.\n- The proposed method performs substantially well in the experiments. \n- This paper is well-organized.\n\n<Weakness>\n\n- It is not clear to see why we need L_reg in the proposed method. The authors state \"We expect this prediction consistency regularization loss in both domains can help induce similar feature representations across domains for the classification task\" in Section 3.3, but is there any theory or empirical evidence? Since this regularization loss is computed for each domain, it should not explicitly induce similar representations across domains. In addition, L_reg is computed in the output space differently from standard contrastive learning such as SimCLR, which should not directly regularize the feature representation.\n- In the experiments, it would be better to include \"source-only\" performance, which is actually the performance of standard partial-label learning, in the comparison. Since the experimental setup is new, showing the performance of naive baselines itself should be important to highlight the necessity of PLUDA methods.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "4: The contributions are significant, and do not exist in prior works.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "6ayaJXlfmv", "reviewer": "Reviewer_jab8", "summary": "", "questions": "", "limitations": "", "rating": 8, "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": "", "summary_of_the_paper": "This work formalizes a new learning scenario called partial label unsupervised domain adaptation (PLUDA) which comprises both PLL and PLUDA problems. A novel PAPLUDA approach is proposed to address the challenging PLUDA problem by three components: classification loss with soft label disambiguation, inter-domain class-prototype alignment, and teacher-student model based contrastive regularization, which mutually benefit from each other to enhance the PLUDA learning. Experiments validate the effectiveness of the proposed method in dealing with the new PLUDA problem.", "summary_of_the_review": "This paper is intriguing and contributes to the filed of partial label learning.", "strength_and_weaknesses": "Strengths:\n1、This paper considers a feature distribution discrepancy problem between the training and test data in partial label learning which is always ignored in the existing PLL methods. The paper formalizes this new learning scenario as PLUDA problem that addressing both PLL and UDA problems in a unified framework is novel.\n2、Exploiting the prototype as the representative of each class to bridge the gap between the source and target domain is promising.\n3、Comprehensive experiments with different noise level show the effectiveness of the proposed method in noise label disambiguation and feature distribution discrepancy.\n\nWeaknesses:\n1、Some abbreviations are unclear, for example: in Figure 1, the expression ‘EMA’ is not mentioned in the paper. I guess it is an update operation for teacher model’s parameter, is not it?\n2、In section 2, the author discuss previous approaches including their weakness that cannot be used in PLUDA problem. But it is not clear which type of existing methods’ weakness the author tries to tackle in the paper.\n3、The author uses the different method to achieve the label disambiguation and pseudo label refinement in the source and target domain respectively. The author tries to achieve the same goal but use different strategy, why?\n4、The key issues of PLUDA can be lied in the feature distribution discrepancy and irrelevant noise label. From the ablation study, we can see that dropping the pseudo-label refinement gets a largest performance degradation, while dropping the class-prototype alignment loss which is also important to the framework gets less performance drop, why?\n5、The problem considered in the paper is novel, but I wonder can we use the off-the-shelf methods to address the new PLUDA problem? Or use the combination of existing PLL and UDA methods? Why?\n6、The feature distribution discrepancy is the core of PLUDA, can you further explain how you can align the feature distribution across domains by the prototype alignment?", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "3gv7RBLL4P", "reviewer": "Reviewer_UB1N", "summary": "", "questions": "", "limitations": "", "rating": 8, "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": "", "summary_of_the_paper": "This paper proposes an unsupervised domain adaptation framework PAPLUDA under the partial label learning scenario which is formalized as a new problem called partial label unsupervised domain adaptation (PLUDA). The proposed PAPLUDA method disambiguates the irrelevant label noise with the help of a teacher-student model and minimizes the discrepancy between the source and target domains by the inter-domain class prototype alignment. Experimental results validate the effectiveness of PAPLUDA in addressing PLUDA problem.", "summary_of_the_review": "This is an interesting paper that formalizes a new learning scenario and addresses both challenging problems in a unified framework. The approach is novel and technically solid.", "strength_and_weaknesses": "Strengths:\n\n1-The domain discrepancy problem is widely existing in many real-world learning applications. This paper explores the problem of discrepancy between training and test data in PLL which is formalized as a challenging PLUDA problem and proposes a novel PAPLUDA framework to address it.\n\n2-The idea of using class prototype to minimize the discrepancy across both domains is interesting.\n\n3-The experiment results demonstrate the effectiveness of the proposed method in addressing both PLL and UDA problems in a unified framework.\n\nWeaknesses:\n\n1-In this paper, the author integrates both PLL and UDA problems and tries to address them in a unified framework. What motivates you to formalize such a new learning scenario? \n\n2-As you stated, GearNet can also address the noise label and UDA problem, why it can’t be used to address the PLUDA problem? Can we modify it for addressing the PLUDA problem? and what’s the difference between WSDA and PLUDA problems.\n\n3-In the approach section, the author uses a one-hot label indicator vector \\hat y_i^s for soft label disambiguation in the source domain, but uses the outputs of the teacher model to update label probability vector p_i^t in the target domain, why?\n\n4-Have you tried to perform the label disambiguation in the source domain and update the label probability vector in the target domain by the same way like Eq.(3) or Eq.(6)? I wonder the different but similar label disambiguation strategy can get a better performance than using the same way?\n\n5-What motivates you deploy the inter-domain class prototype alignment term to alleviate the discrepancy between the source and target domains? Why it works?\n\n6-The class prototype can be obtained in batch-wise by Eq.(5) directly, can you further explain why you update in Eq.(7)?\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "4: The contributions are significant, and do not exist in prior works.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}]

Author rebuttal:

Decision: Accept: poster