A fine-grained analysis of robustness to distribution shifts

Abstract

Robustness to distribution shifts is critical for deploying machine learning models in the real world. Despite this necessity, there has been little work in defining the underlying mechanisms that cause these shifts and evaluating the robustness of algorithms across multiple, different distribution shifts. To this end, we introduce a framework that enables fine-grained analysis of various distribution shifts. We provide a holistic analysis of current state-of-the-art methods by evaluating 19 distinct methods grouped into five categories across both synthetic and real-world datasets. Overall, we train more than 85K models. Our experimental framework can be easily extended to include new methods, shifts, and datasets. We find, unlike previous work (Gulrajani et al, 2020), that progress has been made over a standard ERM baseline; in particular, pretraining and augmentations (learned or heuristic) offer large gains in many cases. However, the best methods are not consistent over different datasets and shifts. Because our experimental framework can be easily extended to include new methods, shifts, and datasets, we hope to encourage future work to use our extendable experimental framework to evaluate new methods over their own distribution shifts and datasets and to evaluate them over multiple distribution shifts to give a more complete picture of a method's effectiveness.

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