Enlarging Feature Support Overlap for Domain Generalization

• URL: http://arxiv.org/abs/2407.05765v1
• Date: Mon, 8 Jul 2024 09:16:42 GMT
• Title: Enlarging Feature Support Overlap for Domain Generalization
• Authors: Yaoyao Zhu, Xiuding Cai, Dong Miao, Yu Yao, Zhongliang Fu,
• Abstract summary: Invariant risk minimization (IRM) addresses this issue by learning invariant features and minimizing the risk across different domains. We propose a novel method to enlarge feature support overlap for domain generalization. Specifically, we introduce Bayesian random data augmentation to increase sample diversity and overcome the deficiency of IRM.
• Score: 9.227839292188346
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep models often struggle with out-of-distribution (OOD) generalization, limiting their real-world applicability beyond controlled laboratory settings. Invariant risk minimization (IRM) addresses this issue by learning invariant features and minimizing the risk across different domains. Thus, it avoids the pitfalls of pseudo-invariant features and spurious causality associated with empirical risk minimization (ERM). However, according to the support overlap theorem, ERM and IRM may fail to address the OOD problem when pseudo-invariant features have insufficient support overlap. To this end, we propose a novel method to enlarge feature support overlap for domain generalization. Specifically, we introduce Bayesian random semantic data augmentation to increase sample diversity and overcome the deficiency of IRM. Experiments on several challenging OOD generalization benchmarks demonstrate that our approach surpasses existing models, delivering superior performance and robustness. The code is available at \url{https://github.com/YaoyaoZhu19/BSDG}.

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