Robust Invariant Representation Learning by Distribution Extrapolation
This addresses the challenge of robust out-of-distribution generalization in deep learning, offering an incremental improvement for machine learning practitioners.
The paper tackles the problem of invariant risk minimization (IRM) failing to outperform empirical risk minimization due to sensitivity to limited environment diversity and over-parameterization, and proposes an extrapolation-based framework that enhances diversity with synthetic distributional shifts, achieving consistent outperformance over state-of-the-art IRM variants in experiments.
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.