SPA: A Graph Spectral Alignment Perspective for Domain Adaptation

Summary

The authors present a new graph Spectral Alignment (SPA) framework for domain adaptation. The method consists of two main components: (i) a coarse graph alignment mechanism that uses a novel spectral regularizer to align domain graphs, and (ii) a fine-grained message propagation module that employs a neighbor-aware self-training mechanism to enhance discriminability in the target domain. Extensive experiments validate the effectiveness of SPA.

Strengths

The paper is exceptionally well-organized, with a clear and concise presentation of the proposed method. The approach itself is highly innovative and addresses a critical tradeoff in graph alignment. The authors have done an excellent job of explaining the core components of their method, including the coarse graph alignment mechanism and the fine-grained message propagation module.

Weaknesses

I have a few concerns regarding some details in the paper that I would like to address: 1. I noticed that the Neighbor-aware Propagation is abbreviated as "L_nas" in the paper. However, I think that "L_nap" would be a more appropriate abbreviation. 2. In Table 4, where the results are presented without L_gsa and L_nas, the average accuracy is reported to be 68.5%. However, I am confused as to why this result is higher than the accuracy reported for CDAN in Table 2. 3. I am also curious about the performance of SPA when it is not combined with existing methods such as CDAN. Have you conducted any experiments to evaluate the single SPA's performance in isolation? 4. I am curious to know whether the graph in question is considered to be heavy. Additionally, I would like to inquire about the number of parameters that are present within the graph.

Questions

See Weaknesses.

Rating

7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.

Confidence

5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.

Soundness

4 excellent

Presentation

3 good

Contribution

3 good

Limitations

No limitations are included.

Summary

The paper proposes a domain adaptation method called Spectral Alignment (SPA). The proposed SPA utilizes spectral distance defined as the difference between eigenvalues of the Laplacian matrix to quantify the distribution gap. Then SPA employs this distance as regularizer for domain alignment. Additionally, SPA incorporates pseudo-labeling for feature aggregation, enhancing the similarity between semantically related nodes. The experimental results on multiple datasets verfy the effectiveness of the proposed algorithm.

Strengths

Different from the previous domain adaptation methods, the paper introduces the spectral distance to measure the domain gap. In addition, the experiment results demonstrate the efficiacy of this idea.

Weaknesses

1. The contribution of the paper is not clear. First, the paper directly employs a spectral distance from [1,20,59] without technical development to measure and narrow the distribution gap. Second, the feature aggregation with pseudo labels has been widely used in domain adaptation. So what the different of the proposed method to the previous work? 2. Figure 1 does not effectively demonstrate the motivation and innovative aspects of this paper. 3. Eq. (5) compute the total loss without consideration of loss scale. Specifically, Eq. (5) adopts four types of loss terms, L_{cls}，L_{adv}，L_{gsa}，L_{nas}. However, there is no trade-off parameters to balance different losses. I am not sure the convergence of the proposed method.

Questions

1. The difference to the previous methods. 2. The convergence analysis of the proposed method.

Rating

6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.

Confidence

4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.

Soundness

4 excellent

Presentation

3 good

Contribution

3 good

Limitations

Yes, the authors say that "The current method for constructing graph spectra is only a starting point and may be inadequate for more difficult scenarios such as universal domain adaptation. Additionally, this method is currently limited to visual classification tasks , and more sophisticated and generic methods to object detection or semantic segmentation are expected in the future" in line 324.

Summary

The paper proposed a graph SPectral Alignment (SPA) framework which uses a coarse graph spectral alignment and neighbor-aware self-training mechanism. The authors compared the proposed method with different UDA methods using several benchmark datasets.

Strengths

The paper is generally well-written and clear. The results show the superior performance of the method in terms of accuracy in different datasets.

Weaknesses

however, it missed some details that need to be completed.

Questions

1. Please add more details that how to construct a dynamic graph, especially for weighted edge and metric function. 2. Please explain the reason why decreasing the distance of graphs is useful for solving da problem. 3. Line 121, ``with n vertices can be from the spectrum of any other graph with n vertices". what's the meaning of n? Do both graphs have to require n? 4. Why uses weighed k-Nearest-Neighbor classification to obtain the pseudo-label？ 5. Please add more analysis about the results of experiments, such as Line 226, ``The experiments shows SPA consistently ourperforms than various of DA methods".

Rating

6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.

Confidence

4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.

Soundness

2 fair

Presentation

2 fair

Contribution

3 good

Limitations

N/A

Summary

This paper proposes a novel graph spectral alignment framework for unsupervised domain adaptation, jointly balancing the inter-domain transferability and the intra-domain discriminability. First, they cast the domain adaptation problem to graph primitives by composing a coarse graph alignment and aligning domain graphs in eigenspaces. Second, they perform a fine-grained message passing in the target domain via a neighbor-aware self-training mechanism. The first alignment method gives rise to coarse-grained topological structures transfer across domains but in a more intrinsic way than restrictive point-wise matching. With the help of fine-grained neighbor-aware propagation, the whole framework is able to refine the transferred topological structure to produce a discriminative domain classifier.

Strengths

1. This paper is easy to follow with clear writing, concise figures, and well-structured formulation in problem definition and methods. 2. The research problem is essential and motivation is clear. This paper gives a new perspective for the essential problem of how to find a suitable utilization of intra-domain information and inter-domain information in unsupervised domain adaptation. 3. The proposed graph spectral alignment method is an attractive method in domain adaptation, which works well and inspires follow-up research. 4. The experiment part is comprehensive, including 4 commonly used datasets in domain adaptation.

Weaknesses

1. To support the claim of balancing transferability and discriminability, the authors offer the figures of visualized features. More ablation studies to express the balance can be offered. 2. The introduction of this paper is less informative. More technical details of the proposed method can be provided there to improve legibility, as well as more comparative description of previous works. 3. Some grammars need to be improved. Line 120-122, the long sentence with two predicates. Line 241, ‘achieve’ should add ‘s’. Line 265, ‘presents’ should remove ‘s’. Line 270, ‘also use’ should add ‘s’. Line 283, ‘, By’ should use period. Line 289-292, ‘propose’, ‘formulate’, ‘calculate’ should add ‘s’. Line 297, ‘comes out’ should remove ‘s’.

Questions

1. The questions are mentioned in weakness. More ablation studies can be offered. More information can be added to introduction. 2. For related work part, more latest references about graph methods can be added. 3. For the Laplacian metrices, you report the results based on two normalized types. Did you try any other types? 4. The authors mentioned the relation between kNN pseudo-labeling and graph propagation, which is confusing for me. Can you explain more on this?

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.

Confidence

5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.

Soundness

3 good

Presentation

4 excellent

Contribution

4 excellent

Limitations

Yes, the authors have addressed the limitations of their work at the end of the paper.

Summary

This work proposed a graph spectral alignment model for unsupervised domain adaptation. They evaluated on several benchmarks and show good performance, especially in DomainNet and Office-Home, compared with existing UDA methods. Generally, the paper is easy to follow.

Strengths

The paper is generally easy to follow and the experimental results on DomainNet and Office-Home are pretty good.

Weaknesses

The novelty of this work is limited. The graph alignment is simply extended from BSP [12] and Graph Matching like SIGMA [39]. The Neighbor-aware Self-training Mechanism is similar to embedding propagation. In this sense, there is limited novelty in terms of the methodology contribution. The performance on other benchmarks like Office-31 and VisDA seem not better than others. The visualization of feature embedding is not informative enough to validate the graph alignment. It is essential to visualize the domain-wise graphs, and how are they aligned? This can show more insights. For example, in Office-Home, their model performs better when A is target domain. Then what is the key reason?

Questions

What are the cross-domain graphs? How are they aligned? What is key difference in terms of the proposed two terms?

Rating

3: Reject: For instance, a paper with technical flaws, weak evaluation, inadequate reproducibility and incompletely addressed ethical considerations.

Confidence

5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.

Soundness

2 fair

Presentation

3 good

Contribution

2 fair

Limitations

The experimntal results are not diverse, mainly on classification performance. The model is incremental over existing methods.

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Official Commit of Submission13641 by Reviewer Ecsg

Thank you for answering my questions. My problems are solved, and I think the paper should be accepted.