Domain Generalization (DG) is an important open problem in machine learning. Deep models are susceptible to domain shifts of even minute degrees, which severely compromises their reliability in real applications. To alleviate the issue, most existing methods enforce various invariant constraints across multiple training domains. However,such an approach provides little performance guarantee for novel test domains in general. In this paper, we investigate a different approach named Contrastive Domain Generalization (CDG), which exploits semantic invariance exhibited by strongly contrastive data pairs in lieu of multiple domains. We present a causal DG theory that shows the potential capability of CDG; together with a regularization technique, Logit Attribution Matching (LAM), for realizing CDG. We empirically show that LAM outperforms state-of-the-art DG methods with only a small portion of paired data and that LAM helps models better focus on semantic features which are crucial to DG.

本文通过引入Contrastive Domain Generalization（CDG）方法，探究以强对比数据对中的语义不变性来进行域泛化的方法，并提出了一种名为Logit Attribution Matching（LAM）的正则化技术来实现CDG，实验结果表明LAM的性能优于现有的DG方法，并帮助模型更好地聚焦于语义特征，这对于域泛化至关重要。

通过逻辑回归归因匹配实现对比领域泛化