The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization

We introduce four new real-world distribution shift datasets consisting of changes in image style, image blurriness, geographic location, camera operation, and more. Overall we find that some methods consistently help with distribution shifts in texture and local image statistics, but these methods do not help with some other distribution shifts like geographic changes.

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• 1 With our new datasets, we take stock of previously proposed methods for improving out-of-distribution robustness and put them to the test.
• 2 We find that using larger models and artificial data augmentations can improve robustness on real-world distribution shifts, contrary to claims in prior work.
• 3 We find improvements in artificial robustness benchmarks can transfer to real-world distribution shifts, contrary to claims in prior work.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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