Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 8: Top 50% of accepted NeurIPS papers, clear accept
review_content: summary: This paper proposes an unsupervised domain adaptation method based on cyclical self-training. There are two main components to the method: 1. a cyclical self-training algorithm that solves a bilevel optimization problem based on an in the inner loop which trains a target classifier with target pseudo-labels  and an outer loop that makes the target classifier perform well on the source domain by updating the shared representations in the outer loop. 2. an uncertainty measure based on the Tsallis entropy, which adaptively minimizes the uncertainty in target pseudo-labels, which replaces the standard Gibbs entropy used by self-training methods. Experiments on computer vision and NLP datasets show that proposed method outperforms baseline methods by a decent margin. Ablation studies show that both components of the algorithm---cyclical self-training and Tsallis entropy---meaningfully contribute to performance improvement. Qualitative and quantitative studies show that target pseudo-label quality is improved by the proposed approach. 
main_review: Strengths:
1. The paper is well-written and organized. The introduction section motivates the problem with current self-training methods using qualitative and quantitative experiments. The proposed approach is supported by theoretical understanding. Experiments and ablation studies help understand how the algorithm works and quantify performance improvement. 

2. Experiments across data domains show good perfomance improvement over state-of-the-art methods, including those tailored to specific domains or dataset types. 

Weakness:
This is not a weakness per se, but the paper would benefit from more analysis of what type of performance improvement is obtained by using this method. Understanding how label shift and covariate shift is tackled and what type of examples are classified better by this method can help spur future research in this problem space. 
limitations_and_societal_impact: See above.
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: Proposes cycle self-training (CST) an unsupervised domain adaptation (UDA) algorithm that cycles between standard self-training of a target classifier on (source model generated) target pseudolabels, and a new cycle self-training step that updates backbone representations so as to generalize the target classifier to the source domain. An additional self-training objective based on the Tsallis entropy is introduced. Theoretical and empirical results are presented on several standard UDA benchmarks.
main_review: Originality

The proposed approach is novel and original.

Quality

– The paper does well to analyze and clearly describe some of the shortcomings of self-training (Sec 2.1). 

– The proposed approach, although slightly complex, is well-designed, explained, and well-supported by theoretical analysis (Sec 4).

– The performance gains of the proposed approach are consistent and promising, and the paper does well to present results on several benchmarks (including additional ones in the supplement) across vision and language tasks, and compare to a wide range of baselines, including strong baselines derived from combining existing methods. 

Clarity

– The paper is well-written and easy to follow.

– [minor] The flow of logic in Figure 1 is somewhat difficult to follow and it took me multiple reads. It would be good to try and simplify it.

– [minor] In Eq 3-5, it would be good to include what instances (source / target) the objective operates on

Significance

The paper focuses on the UDA problem that is of considerable research interest and the proposed approach improves upon prior work in a wide variety of settings.

Overall

This is a well-written paper that proposes an interesting, effective, and principled UDA approach. I recommend acceptance.

---Post-Rebuttal----

I have read the author response and other reviews, and would like to keep my accept rating.
limitations_and_societal_impact: Authors adequately addressed the limitations and potential negative societal impact.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: This paper presents a novel approach for unsupervised domain adaptation (UDA) based on cycle self-training. Instead of conventional self-training, there are a generated and two attached classifiers with different purposes. The source classifier, mainly trained on the source loss, is applied to infer pseudo target labels. The target classifier is trained by the self-training loss based on the pseudo labels provided by the source classifier.  The authors also propose the Tsallis entropy as the regularization term, which can be seen as a side contribution.
main_review: Strengths:

+ The paper is organized in clear logic, which is easier to read. The discussion of related works is comprehensive enough to cover most of the topics in the field.

+ There is a detailed analysis of self-training in different cases: with or without domain shift, distribution change, etc. It is helpful to deeply understand the effects of pseudo labels in the DA setting.

+ Tsallis entropy is an interesting idea that seems to fit better with the DA problem than the conventional entropy loss. And the discussion of its motivation is comprehensive and clear.

Weaknesses:

-The cycle self-training seems not novel and too simple. Some previous works on the tri-training-based DA have tried similar ideas where a fixing classifier is used to help another classifier. And I am not quite convinced by the design of the source classifier, which seems not to be trained by the target samples. If so, it is hard for me to believe that it can infer the accurate target pseudo labels without seeing them. Will the pseudo labels have biased towards the source domain? How to address such a problem.

-The experimental results are not strong enough. Although it outperforms the SOTA SSL methods and some baseline UDA methods, some ignored UDA works such as [1] and [2] can beat the proposed method by a large margin. 



Other Comments:

1. The right subfigure of Fig2 is too small to read. It will be much better if enlarging it.

2. Please provide which dataset is used in the caption of Fig 2.

3. In lines 36 – 42, the motivation of the reverse step is not convincing enough. The target pseudo labels informative to the source domain may also be harmful. The authors should provide more solid experimental results to support such a claim.

[1] Contrastive Adaptation Network for Unsupervised Domain Adaptation. CVPR 2019.
[2] FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation. CVPR 2021.

After rebuttal:
My main concerns are addressed. I'd like to increase my rating score. Thanks for the detailed feedback.
limitations_and_societal_impact: Yes. The authors provide adequate discussion of limitations and potential negative societal impact.
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: The paper tackles the problem of domain adaptation with a newly proposed cycle self-training algorithm. Given observed that the pseudo labels are noisy and existing de-noising methods require ad-hoc hyper-parameters for specific tasks, the authors attempt to progressively refine the pseudo labels by the capability of the network itself. With the intuition of transferring the source-domain knowledge to the target domain, the authors propose to regularize the pseudo labels with a cyclic training pipeline. Tsallis Entropy is further introduced to improve the label quality. Experiments on both visual classification and linguistic sentiment classification indicate the effectiveness of the introduced method.
main_review: Strengths:

+ The paper is well written and easy to follow. The figures and charts are nice and help to understand.

+ The analysis of limitations of existing self-training methods in Sec. 2.1 well motivates the introduce method.

+ The authors carefully analyze the introduced method both theoretically and empirically. 

+ The introduced method is simple but effective. It seems easy to implement and I believe the results can be well reproduced. 

+ The introduced Tsallis Entropy can be seamlessly integrated into existing methods, e.g., FixMatch, and achieved significant performance gains as shown in Table 5.


I am overall satisfied with this paper and would like to give some comments that may make this paper better.

1. The notations are a bit complex.

2. Despite that the introduced method is very simple, it is also necessary to compare with more SoTA methods (in CVPR2020/ECCV2020/…) in the experimental part (Table 2/3/…). 

3. Generalizing this method to open-set domain adaptation would make it more applicable in real-world scenarios.


After rebuttal: My concerns have been well addressed. I recommend accepting this paper.
limitations_and_societal_impact: The introduced cycle self-training could only be applied when two domains share the same label set. How to properly generalize this method to real-world scenarios (e.g., open-set) is important.
review_title: null

Meta-review: The reviewers unanimously agree that the paper should be accepted. I find the idea of the paper interesting and the empirical results seem encouraging. I am not convinced that the presented algorithm in its full complexity can be theoretically studied. The experimental results are encouraging but still falling behind the state-of-the-art. Notation is cluttered and exposition is not as clear as it should be. I recommend acceptance as a poster, but please incorporate reviewers' feedback to improve the paper.
Decision: Accept (Poster)