Venue: ICLR 2022 Conference

Reviews:

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This paper proposes a pairwise adversarial training approach for class-imbalanced domain adaptation. Specifically, the adversarial samples are generated from the interpolated line of the aligned pairwise source domain samples and target domain samples. The generated adversarial data can augment the training data and help enhance the robustness of models.
main_review: 
- Since the target domain is unlabeled, the interpolation between a source sample and a target sample of the same class is not reliable, which may face the same error accumulation issue as the pseudo-labeling methods. If the pseudo-label is not correct, the augmented data may not only cause the error accumulation but also damage the domain alignment.

- How does the interpolated adversarial sample generation method deal with the data imbalance issue in the target domain? It seems that the generation process that generates data merely resorts to the guidance of the class probability of the source domain data. 

- The interpolation method looks quite general, will this method also work well on the UDA problem?

- In experiments, more discussions are required for explaining why the proposed method performs worse than Sentry (Prabhu et al., 2021).

summary_of_the_review: In general, the proposed method is somewhat novel. However, the method is not very well justified and some parts of the method are unclear.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This work proposes a method for solving the UDA problem with imbalanced class, which is a sub-problem of UDA. The challenge lies in how to handle the difficulties introduced by imbalanced classes. To this end, this work proposes a new data augmentation strategy, that is taking the interpolation of two samples from the same class but from different domains as the augmented samples. The traditional MMD loss and a class centroid distance based loss are also imposed for the model training. Experiments on multiple benchmark datasets are conducted. 
main_review: Strength:

1. The interpolation based data augmentation method is interesting. 
2. The overall approach is simple and easy to implement. 

Weakness:
1. Novelty is limited. The interpolation is similar to mix-up and follow-up works. Relation with mix-up should be discussed here.
2. The proposed method does not outperform the baseline Sentry. 
3. Some statements are based on intuition but not well supported. For instance, the proposed method is claimed to help the model generalize better on the target domain for minority classes but the corresponding experiments are lacking. 
summary_of_the_review: Overall, this work introduces a neat idea, that is taking the interpolation of two anchor samples to augment the minority class. But overall the paper is lacking in depth and the experiment results are not very convincing.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This paper proposes a new method called Pairwise Adversarial Training (PAT) that augments training data for class-imbalanced domain adaptation (CDA). Different from vanilla unsupervised domain adaptation, the label distributions of different distributions are quite different in CDA. The proposed PAT approach mainly consists of two part, centroid alignment (CA) and interpolated adversarial samples (IAS). Experiments on several benchmarks verify the effectiveness of PAT for the CDA problem. 
main_review: Strength:
1. the studied problem called class-imbalanced domain adaptation is interesting and practical
2. the proposed method especially the IAS part is simple and easy to understand
3. the results on CDA are impressive 

Weakness:
1. the part called semantic centroid alignment (CA) within the proposed PAT is commonly used in the domain adaptation field (Xie et al., 2018), and the moving average strategy and the balancing strategy are also not new

2. the authors aim to address the CDA problem, however, it sounds that PAT could work well for vaninna UDA problems, the authors are suggested to perform comparison with UDA methods on these datasets. For example, the subsets of the VISDA-2017 dataset are not label-balanced, PAT could be compared with recent UDA methods [a-b] directly w.r.t. the per-class accuracy.

[a]. Liang, Jian, Dapeng Hu, and Jiashi Feng. "Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation." International Conference on Machine Learning. PMLR, 2020.

[b]. Na, Jaemin, et al. "FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021.

3. besides, it seems that PAT is independent on MDD (adopted in this paper), could the authors further show its combination with else domain alignment techniques like CDAN? And the results in Table 4 only show 3 out of 6 tasks on the OfficeHome dataset, and the contribution of IAS seems not significant (I think IAS is main contribution of this paper).

4. minor concerns: how to determine the parameters like \alpha and \beta within PAT, results in Fig.7 show PAT is somewhat sensitive to these parameters. 
summary_of_the_review: Class-imbalanced domain adaptation is an interesting and important problem in the UDA field. The strategy of interpolated adversarial samples (IAS) is new and interesting, however, the overall novelty of the proposed method sounds not much high for a top-tier conference. In addition, the experiments do not fully verify the effectiveness of PAT especially for IAS, thus I vote for "weak reject."
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This paper proposes an adversarial data augmented method to solve the class-imbalanced problem in domain adaptation. 
main_review: This paper solve the class-imbalanced problem from two aspects:
1. pairwise adversarial training and generate the samples on the interpolated line from a source sample to a target sample of the same class. And samples from the minority class will have larger chance.
2. Align the conditional feature distributions of source and target domains by explicitly matching the centroids of two domains.

However, my major concerns are as follows:
1. During the data generating process, the pseudo-labels of the target data are still not accurate. Won't there still exist error accumulation?
2. The improvements of the experimental results are not obvious.
summary_of_the_review: The novelty is limited and some statements should be explained further so that the advantage of the proposed method could be more obvious.
review_title: null

Meta-review: This paper aims to address the imbalanced class problem in unsupervised domain adaptation. The challenge lies in how to handle the difficulties introduced by imbalanced classes. To this end, this work proposes a new data augmentation strategy by taking the interpolation of two samples from the same class but from different domains as the augmented samples. The experiments demonstrate promising performance on the class-imbalanced domain adaptation datasets.

However, there are several concerns raised by the reviewers. 1) The interpolation between a source and target sample of the same class can potentially be unreliable as the pseudo label methods. 2) Some statements are based on intuition but not well supported by either theoretical analysis or experimental evaluations. 3) The proposed method is inferior to baseline methods on some datasets, it would be helpful to have further analysis of the advantages and limitations of the proposed method. 

Overall, the paper provides some new and interesting ideas. However, given the above concerns, the novelty and significance of the paper will degenerate. More discussions on the principles behind the proposed method and more experimental studies are needed. Addressing the concerns needs a significant amount of work. Although we think the paper is not ready for ICLR in this round, we believe that the paper would be a strong one if the concerns can be well addressed.
Decision: Reject