Why Domain Generalization Fail? A View of Necessity and Sufficiency

TL;DR

This paper analyzes why domain generalization often fails in practice, emphasizing the importance of necessary and sufficient conditions for generalization, and proposes a method to better satisfy these conditions to improve performance.

Contribution

It introduces a theoretical framework distinguishing necessary and sufficient conditions for domain generalization and proposes a novel subspace alignment method to satisfy these conditions.

Findings

Existing DG methods mainly satisfy sufficient conditions, neglecting necessary ones.

The proposed method improves generalization by maintaining necessary conditions.

Empirical results show enhanced performance on DG benchmarks.

Abstract

Despite a strong theoretical foundation, empirical experiments reveal that existing domain generalization (DG) algorithms often fail to consistently outperform the ERM baseline. We argue that this issue arises because most DG studies focus on establishing theoretical guarantees for generalization under unrealistic assumptions, such as the availability of sufficient, diverse (or even infinite) domains or access to target domain knowledge. As a result, the extent to which domain generalization is achievable in scenarios with limited domains remains largely unexplored. This paper seeks to address this gap by examining generalization through the lens of the conditions necessary for its existence and learnability. Specifically, we systematically establish a set of necessary and sufficient conditions for generalization. Our analysis highlights that existing DG methods primarily act as regularization mechanisms focused on satisfying sufficient conditions, while often neglecting necessary ones. However, sufficient conditions cannot be verified in settings with limited training domains. In such cases, regularization targeting sufficient conditions aims to maximize the likelihood of generalization, whereas regularization targeting necessary conditions ensures its existence. Using this analysis, we reveal the shortcomings of existing DG algorithms by showing that, while they promote sufficient conditions, they inadvertently violate necessary conditions. To validate our theoretical insights, we propose a practical method that promotes the sufficient condition while maintaining the necessary conditions through a novel subspace representation alignment strategy. This approach highlights the advantages of preserving the necessary conditions on well-established DG benchmarks.