Learning a domain-invariant representation has become one of the most popular approaches for domain adaptation/generalization. In this paper, we show that the invariant representation may not be sufficient to guarantee a good generalization, where the labeling function shift should be taken into consideration. Inspired by this, we first derive a new generalization upper bound on the empirical risk that explicitly considers the labeling function shift. We then propose Domain-specific Risk Minimization (DRM), which can model the distribution shifts of different domains separately and select the most appropriate one for the target domain. Extensive experiments on four popular domain generalization datasets, CMNIST, PACS, VLCS, and DomainNet, demonstrate the effectiveness of the proposed DRM for domain generalization with the following advantages: 1) it significantly outperforms competitive baselines; 2) it enables either comparable or superior accuracies on all training domains comparing to vanilla empirical risk minimization (ERM); 3) it remains very simple and efficient during training, and 4) it is complementary to invariant learning approaches.

本文提出了基于领域特定风险最小化（DRM）的方法，旨在通过利用源域信息和适应性差的估计和最小化来弥合领域间差异以实现领域通用性，并在不同分布漂移设置下显着优于竞争基准。

针对领域特定风险的最小化以实现超出分布的泛化