How Does Distribution Matching Help Domain Generalization: An Information-theoretic Analysis
arxiv(2024)
摘要
Domain generalization aims to learn invariance across multiple training
domains, thereby enhancing generalization against out-of-distribution data.
While gradient or representation matching algorithms have achieved remarkable
success, these methods generally lack generalization guarantees or depend on
strong assumptions, leaving a gap in understanding the underlying mechanism of
distribution matching. In this work, we formulate domain generalization from a
novel probabilistic perspective, ensuring robustness while avoiding overly
conservative solutions. Through comprehensive information-theoretic analysis,
we provide key insights into the roles of gradient and representation matching
in promoting generalization. Our results reveal the complementary relationship
between these two components, indicating that existing works focusing solely on
either gradient or representation alignment are insufficient to solve the
domain generalization problem. In light of these theoretical findings, we
introduce IDM to simultaneously align the inter-domain gradients and
representations. Integrated with the proposed PDM method for complex
distribution matching, IDM achieves superior performance over various baseline
methods.
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