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.