Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: The authors proposed a method for source-free unsupervised domain adaptation (SFUDA) task. The key idea is to combine the advantages of global alignment [1] and feature consistency [2]. The authors divided the target data into source-like and target-specific samples and 
treat them by different learning methods. The authors demonstrate extensive experiments on three datasets and verify the performance of the proposed method
strengths_and_weaknesses: Strengths
1.The paper is well-organized and easy to follow.
2. The "divide and contrast" strategy is simple but effective; it can exploit both the global and local structures of target data.
3. The proposed Exponential-MMD loss is novel, it also makes sense to align the source-like and target-specific samples to reduce distribution mismatch.
4. The experiment and ablations are sufficient to support the conclusion.

Weaknesses
1. The section of preliminaries and analysis is a bit unclear to introduce the task.
2. A more recent work that outperforms proposed approach ([a]) is not compared against.
[a] Liang, Jian, et al. "Source data-absent unsupervised domain adaptation through hypothesis transfer and labeling transfer." IEEE Transactions on Pattern Analysis and Machine Intelligence (2021).
3. Eqn. 4 and Eqn. 5 share the same parameter \tau but they are different from each other. The threshold \tau in Eqn. 4 is set to 0.95 according to supplementary material following [47], however, I could not find the reference [47]. Besides, the choice of the threshold \tau is not included in the ablation study, it would be necessary to see the ablation of choosing the threshold \tau.
4. As described in Discussion(L200-L207), unlike previous method [3], Eqn. 5 jointly achieves class-wise adaptation and instance-wise adaptation. It would be interesting if the authors could compare the proposed loss with separate class-wise adaptation and instance-wise adaptation losses.
5. When do the method update the source-like set and class centroids, after one batch or one epoch.
questions: 1. When do the method update the source-like set and class centroids, after one batch or one epoch？
limitations: Yes
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This paper presents a source-free domain adaptation method. Previous methods either use self-supervised pseudo labeling to conduct class-wise global alignment or leverage local structure to enforce feature consistency. This work combines the idea of both. The proposed method divides target samples into source-like and target-specific ones. Source-like samples are used for global class clustering and target-specific samples are used for learning local structures. The two are further aligned using maximum mean discrepancy loss.
strengths_and_weaknesses: Strengths are as follows.

The idea is interesting. The target samples are divided based on confidence output of source classifier. Different groups are treated differently, either globally in class-level or locally in instance-level. Two different groups are aligned to encourage consistency that is also interesting. The presentation is generally good. Ablation study is conducted for each part.

Weaknesses are as follows.

The major weakness is the performance compared to prior work. It shows that the results of the proposed method are just marginally good compared to [2] and [3] in 2 out of 3 datasets (table 1 and table 3). 

The performance is good in table 2. Can the authors explain more about the baseline results? For example, how the official code was used and how the authors ensure the hyper parameters are reasonably well tuned? 

questions: See weaknesses above.
limitations: No
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This paper proposes a new source-free unsupervised domain adaptation method named DaC. The key idea is to leverage the advantages of existing “global” methods and “local” counterparts. Specifically, DaC uses the source model to split the target data into source-like and target-specific samples. After that, an adaptive contrastive learning strategy is used to achieve class-wise adaptation (global) and local consistency (local). Finally, MMD is used to minimize the distribution mismatch between source-like samples and target-specific samples. The proposed method achieves the best performance on widely used benchmarks.

strengths_and_weaknesses: ---

Originality: the proposed method is a combination of well-known techniques, but achieves good performance. Integrating contrastive learning into source-free domain adaptation is novel and brings new insights into this community.

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Quality: 

Strengths: this work is technically sound with theoretical proof and empirical evaluation. The effectiveness of the proposed regularizations are evaluated on various downstream tasks. The work is complete.

Weakness: (1) the major concern is the performance. As shown in Table 1 and Table 3, compared to existing SOTA methods (NRC and CPGA), the performance improvement is quite limited. Although it outperforms the baseline SHOT by a large margin, it is still hard to convince readers. (2) The proposed method is not evaluated on Digit datasets (MNIST, SVHN, and USPS) and the Office31 dataset which are two important benchmarks. Any reason behind this?

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Clarity: this paper is well written and well organized. It is easy to follow. Detailed implementation details are also provided in the supplementary.

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Significance: compared to the baseline SHOT, the result is important. Specifically, the proposed method brings a larger improvement than SHOT. However, it is not clear to me whether other methods can borrow the same idea and get performance improvement.

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questions: 1. Line 262: change “trains” to “train”
2. Any reason for missing the experiments on Digit datasets and the Office31 dataset?

limitations: Not applicable.
review_title: null

final_score: rating: 8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper is about unsupervised domain adaptation when the source data is unavailable at the time of adaptation. Authors term this problem as source-free unsupervised domain adaptation (SFUDA). Authors make the insightful  observation that existing SFUDA techniques use all pseudo labels on target data including noisy labels or enforce local feature consistency at the expense of being source-biased. Motivated by these observations, authors propose a new SFUDA approach that they call Divide and Contrast (DaC) which divides the target data into two disjoint groups: source-like samples and target-specific samples. An adaptive learning framework is presented that treats each of these target sample groups differently during the training process. Authors present both theoretical results and numerical results that illustrate the superiority of DaC over state of the art methods for SFUDA.
strengths_and_weaknesses: Strengths
Authors illustrate well (e.g., Fig. 1) the tradeoffs involved in the global and local approaches to SFUDA.

The proposed DaC approach for SFUDA is a novel approach to address the limitations of prior methods to SFUDA.

Authors present theoretical derivations to justify the DaC approach.

Numerical results shown on multiple datasets are pretty convincing of the superiority of DaC approach.

Weaknesses
"Memory bank" is invoked early on without sufficient explanation. It is explained in Section 4.2.2, but may make the paper more readable if this introduction could be provide earlier in the manuscript.

No error bars are provided for the numerical results.
questions: 1. Line 51: Provide a brief explanation for the "memory bank".
2. Line 260: What is SHOT? Cite a reference for that.

limitations: Authors do not explicitly discuss the limitations of the proposed method, but mention semi-supervised SFUDA  and source-free open-set DA as possible research extension topics. If space permits, it may be interesting to provide a few more sentences about each of these topics and how DaC might fare in those cases.
review_title: null

Meta-review: metareview: This paper proposes a relatively complicated method for source-free unsupervised domain adaptation, which integrates several techniques into a divide and contrast framework. The idea of dividing the target data into source-like subset and target-specific subset and employing global alignment and feature consistency for each subset is novel when the source data is inaccessible. The contrastive learning and memory-based MMD are novel in the context of source-free domain adaptation and introduce theoretical benefits in terms of the expansion theory and domain alignment theory, respectively. Reviewers were on the positive side while holding some concerns on the marginal improvement over the SoTA methods, which were addressed in the author rebuttal. AC generally agreed that the paper has introduced a novel and solid contribution to the field, with a nice connection between algorithmic methods and theoretical insights, and  recommended the paper for acceptance. Authors are suggested to incorporate all rebuttal material in the revision and if possible, to work out a recipe for easing the adoption of their relatively complicated framework that comes with many modules and loss terms.
confidence: Certain
Decision: Accept