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We introduce four new real-world distribution shift datasets consisting ofchanges in image style, image blurriness, geographic location, cameraoperation, and more. With our new datasets, we take stock of previouslyproposed methods for improving out-of-distribution robustness and put them tothe test. We find that using larger models and artificial data augmentationscan improve robustness on real-world distribution shifts, contrary to claims inprior work. We find improvements in artificial robustness benchmarks cantransfer to real-world distribution shifts, contrary to claims in prior work.Motivated by our observation that data augmentations can help with real-worlddistribution shifts, we also introduce a new data augmentation method whichadvances the state-of-the-art and outperforms models pretrained with 1000 timesmore labeled data. Overall we find that some methods consistently help withdistribution shifts in texture and local image statistics, but these methods donot help with some other distribution shifts like geographic changes. Ourresults show that future research must study multiple distribution shiftssimultaneously, as we demonstrate that no evaluated method consistentlyimproves robustness.

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