Robust Invariant Representation Learning by Distribution Extrapolation

• URL: http://arxiv.org/abs/2505.16126v2
• Date: Fri, 23 May 2025 01:48:03 GMT
• Title: Robust Invariant Representation Learning by Distribution Extrapolation
• Authors: Kotaro Yoshida, Konstantinos Slavakis,
• Abstract summary: Invariant risk minimization (IRM) aims to enable out-of-distribution generalization in deep learning.<n>Existing approaches -- including IRMv1 -- adopt penalty-based single-level approximations.<n>A novel framework is proposed that enhances environmental diversity by augmenting the IRM penalty through synthetic distributional shifts.
• Score: 3.5051814539447474
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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