Domain Generalization via Invariant Representation under Domain-Class Dependency
Learning domain-invariant representation is a dominant approach for domain generalization, where we need to build a classifier that is robust toward domain shifts induced by change of users, acoustic or lighting conditions, etc. However, prior domain-invariance-based methods overlooked the underlying dependency of classes (target variable) on source domains during optimization, which causes the trade-off between classification accuracy and domain-invariance, and often interferes with the domain generalization performance. This study first provides the notion of domain generalization under domain-class dependency and elaborates on the importance of considering the dependency by expanding the analysis of Xie et al. (2017). We then propose a method, invariant feature learning under optimal classifier constrains (IFLOC), which explicitly considers the dependency and maintains accuracy while improving domain-invariance. Specifically, the proposed method regularizes the representation so that it has as much domain information as the class labels, unlike prior methods that remove all domain information. Empirical validations show the superior performance of IFLOC to baseline methods, supporting the importance of the domain-class dependency in domain generalization and the efficacy of the proposed method for overcoming the issue.
 In this paper, the author(s) propose a method, invariant feature learning under optimal classifier constrains (IFLOC), which maintains accuracy while improving domain-invariance. Here is a list of suggestions that will help the author(s) to improve this paper. 1.The paper explains the necessity and effectiveness of the method from the theoretical and experimental aspects, but the paper does not support the innovation point enough, and the explanation is too simple. 2.In this paper, Figure3-(b) shows that the classification accuracy of IFLOC-abl method decreases a lot when γ is taken to 0. Figure3-(c) shows that the domain invariance of IFLOC-abl method becomes significantly worse when γ is 10. The author(s) should explain the reasons in detail. 3. The lack of analysis on domain-class dependency of each dataset makes the analysis of experimental results weak.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Domain Generalization via Invariant Representation under Domain-Class Dependency
Learning domain-invariant representation is a dominant approach for domain generalization, where we need to build a classifier that is robust toward domain shifts induced by change of users, acoustic or lighting conditions, etc. However, prior domain-invariance-based methods overlooked the underlying dependency of classes (target variable) on source domains during optimization, which causes the trade-off between classification accuracy and domain-invariance, and often interferes with the domain generalization performance. This study first provides the notion of domain generalization under domain-class dependency and elaborates on the importance of considering the dependency by expanding the analysis of Xie et al. (2017). We then propose a method, invariant feature learning under optimal classifier constrains (IFLOC), which explicitly considers the dependency and maintains accuracy while improving domain-invariance. Specifically, the proposed method regularizes the representation so that it has as much domain information as the class labels, unlike prior methods that remove all domain information. Empirical validations show the superior performance of IFLOC to baseline methods, supporting the importance of the domain-class dependency in domain generalization and the efficacy of the proposed method for overcoming the issue.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 In this paper, the author(s) propose a method, invariant feature learning under optimal classifier constrains (IFLOC), which maintains accuracy while improving domain-invariance. Here is a list of suggestions that will help the author(s) to improve this paper. 1.The paper explains the necessity and effectiveness of the method from the theoretical and experimental aspects, but the paper does not support the innovation point enough, and the explanation is too simple. 2.In this paper, Figure3-(b) shows that the classification accuracy of IFLOC-abl method decreases a lot when γ is taken to 0. Figure3-(c) shows that the domain invariance of IFLOC-abl method becomes significantly worse when γ is 10. The author(s) should explain the reasons in detail. 3. The lack of analysis on domain-class dependency of each dataset makes the analysis of experimental results weak.
assistant
 In this paper, the author(s) propose a method, invariant feature learning under optimal classifier constrains (IFLOC), which maintains accuracy while improving domain-invariance. Here is a list of suggestions that will help the author(s) to improve this paper. 1.The paper explains the necessity and effectiveness of the method from the theoretical and experimental aspects, but the paper does not support the innovation point enough, and the explanation is too simple. 2.In this paper, Figure3-(b) shows that the classification accuracy of IFLOC-abl method decreases a lot when γ is taken to 0. Figure3-(c) shows that the domain invariance of IFLOC-abl method becomes significantly worse when γ is 10. The author(s) should explain the reasons in detail. 3. The lack of analysis on domain-class dependency of each dataset makes the analysis of experimental results weak.