An Empirical Study of Invariant Risk Minimization
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Invariant risk minimization (IRM; Arjovsky et al., 2019) is a recently proposed framework designed for learning predictors that are invariant to spurious correlations across different training environments. Because IRM does not assume that the test data is identically distributed as the training data, it can allow models to learn invariances that generalize well on unseen and out-of-distribution (OOD) samples. Yet, despite this theoretical justification, IRM has not been extensively tested across various settings. In an attempt to gain a better understanding of IRM, we empirically investigate several research questions using IRMv1, which is the first practical algorithm proposed in (Arjovsky et al., 2019) to approximately solve IRM. By extending the ColoredMNIST experiment from (Arjovsky et al., 2019) in multiple ways, we find that IRMv1 (i) performs better as the spurious correlation varies more widely between training environments, (ii) learns an approximately invariant predictor when the underlying relationship is approximately invariant, and (iii) can be extended to multiple environments, multiple outcomes, and different modalities (i.e., text). We hope that this work will shed light on the characteristics of IRM and help with applying IRM to real-world OOD generalization tasks.
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