Workshop: Workshop on Machine Learning Safety

What 'Out-of-distribution' Is and Is Not

Sebastian Farquhar · Yarin Gal


Researchers want to generalize robustly to ‘out-of-distribution’ (OOD) data. Unfortunately, this term is used ambiguously causing confusion and creating risk—people might believe they have made progress on OOD data and not realize this progress only holds in limited cases. We critique a standard definition of OOD—difference-in-distribution—and then disambiguate four meaningful types of OOD data: transformed-distributions, related-distributions, complement-distributions, and synthetic-distributions. We describe how existing OOD datasets, evaluations, and techniques fit into this framework. We provide a template for researchers to carefully present the scope of distribution shift considered in their work.

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