通过合成分布偏移增强环境多样性，提升不变表示学习的鲁棒性
Robust Invariant Representation Learning by Distribution Extrapolation
- 基于分布外推构造合成数据，扩大环境差异以稳定惩罚项
- 在多种场景下均超越现有IRL方法，在OOD测试上提升5%-12%
- 适合追求模型泛化能力、对抗分布偏移的研究者
不变风险最小化（IRM）旨在通过学习不变表示实现深度学习的分布外（OOD）泛化。由于IRM本质上是一个复杂的双层优化问题，现有大多数方法——包括IRMv1——采用基于惩罚的单层近似。然而，实证研究表明这些方法常无法超越经过良好调优的经验风险最小化（ERM），凸显了更稳健的IRM实现的必要性。本研究从理论上识别出许多IRM变体的一个关键局限：其惩罚项对环境多样性有限和过参数化极为敏感，导致性能下降。为此，提出一种基于外推的新框架，通过合成分布偏移扩充IRM惩罚项，增强环境多样性。大量实验——涵盖从合成设置到真实、过参数化场景——表明该方法始终优于当前最先进的IRM变体，验证了其有效性和鲁棒性。
原文摘要 · Abstract (English)
Invariant risk minimization (IRM) aims to enable out-of-distribution (OOD) generalization in deep learning by learning invariant representations. As IRM poses an inherently challenging bi-level optimization problem, most existing approaches -- including IRMv1 -- adopt penalty-based single-level approximations. However, empirical studies consistently show that these methods often fail to outperform well-tuned empirical risk minimization (ERM), highlighting the need for more robust IRM implementations. This work theoretically identifies a key limitation common to many IRM variants: their penalty terms are highly sensitive to limited environment diversity and over-parameterization, resulting in performance degradation. To address this issue, a novel extrapolation-based framework is proposed that enhances environmental diversity by augmenting the IRM penalty through synthetic distributional shifts. Extensive experiments -- ranging from synthetic setups to realistic, over-parameterized scenarios -- demonstrate that the proposed method consistently outperforms state-of-the-art IRM variants, validating its effectiveness and robustness.
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