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arXiv · 2407.05765

Invariance Principle Meets Vicinal Risk Minimization

Yaoyao Zhu·Xiuding Cai·Yingkai Wang·Dong Miao·Zhongliang Fu·Xu Luo
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Abstract

Deep learning models excel in computer vision tasks but often fail to generalize to out-of-distribution (OOD) domains. Invariant Risk Minimization (IRM) aims to address OOD generalization by learning domain-invariant features. However, IRM struggles with datasets exhibiting significant diversity shifts. While data augmentation methods like Mixup and Semantic Data Augmentation (SDA) enhance diversity, they risk over-augmentation and label instability. To address these challenges, we propose a domain-shared Semantic Data Augmentation (SDA) module, a novel implementation of Variance Risk Minimization (VRM) designed to enhance dataset diversity while maintaining label consistency. We further provide a Rademacher complexity analysis, establishing a tighter generalization error bound compared to baseline methods. Extensive evaluations on OOD benchmarks, including PACS, VLCS, OfficeHome, and TerraIncognita, demonstrate consistent performance improvements over state-of-the-art domain generalization methods.

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Yaoyao Zhu, Xiuding Cai, Yingkai Wang, Dong Miao, Zhongliang Fu, Xu Luo. 2024-07-08. Invariance Principle Meets Vicinal Risk Minimization. https://arxiv.org/abs/2407.05765

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