Domain generalization aims to learn a generalizable model to unseen target domains from multiple source domains. Various approaches have been proposed to address this problem. However, recent benchmarks show that most of them do not provide significant improvements compared to the simple empirical risk minimization (ERM) in practical cases. In this paper, we analyze how ERM works in views of domain-invariant feature learning and domain-specific gradient normalization. In addition, we observe that ERM converges to a loss valley shared over multiple training domains and obtain an insight that a center of the valley generalizes better. To estimate the center, we employ stochastic weight averaging (SWA) and provide theoretical analysis describing how SWA supports the generalization bound for an unseen domain. As a result, we achieve state-of-the-art performances over all of widely used domain generalization benchmarks, namely PACS, VLCS, OfficeHome, TerraIncognita, and DomainNet with large margins. Further analysis reveals how SWA operates on domain generalization tasks.

本文探讨了领域泛化方法的有效性，展示了理论证明找到平坦最小值可以获得更小的领域泛化差距并提出了用于实现此目标的简单而有效的方法SWAD，并展示了其在五个领域泛化基准测试中的表现。

SWAD：通过寻找平坦最小值实现域泛化