Papers › The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, Justin Gilmer
We introduce four new real-world distribution shift datasets consisting of changes in image style, image blurriness, geographic location, camera operation, and more. With our new datasets, we take stock of previously proposed methods for improving out-of-distribution robustness and put them to the test. We find that using larger models and artificial data augmentations can improve robustness on real-world distribution shifts, contrary to claims in prior work. We find improvements in artificial robustness benchmarks can transfer to real-world distribution shifts, contrary to claims in prior work. Motivated by our observation that data augmentations can help with real-world distribution shifts, we also introduce a new data augmentation method which advances the state-of-the-art and outperforms models pretrained with 1000 times more labeled data. 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. Our results show that future research must study multiple distribution shifts simultaneously, as we demonstrate that no evaluated method consistently improves robustness.
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Code
Syntology Ran 13 of 14 code samples harvested from 1 repository linked to this paper; 1 has no recorded run. Of those that ran: 12 ran · our draft was wrong; 1 ran with no contract checked.
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Domain Generalization | ImageNet-C | DeepAugment (ResNet-50) | mean Corruption Error (mCE) | 60.4 | #36 of 47 | Archive leaderboard | report |
| Domain Generalization | ImageNet-R | DeepAugment+AugMix (ResNet-50) | Top-1 Error Rate | 53.2 | #29 of 39 | Archive leaderboard | report |
| Domain Generalization | ImageNet-R | DeepAugment (ResNet-50) | Top-1 Error Rate | 57.8 | #33 of 39 | Archive leaderboard | report |
| Domain Generalization | VizWiz-Classification | ResNet-50 (deepaugment) | Accuracy - All Images | 41.3 | #30 of 90 | Archive leaderboard | report |
| Domain Generalization | VizWiz-Classification | ResNet-50 (deepaugment) | Accuracy - Clean Images | 46 | #30 of 90 | Archive leaderboard | report |
| Domain Generalization | VizWiz-Classification | ResNet-50 (deepaugment) | Accuracy - Corrupted Images | 34.9 | #30 of 90 | Archive leaderboard | report |
| Domain Generalization | VizWiz-Classification | ResNet-50 (deepaugment+augmix) | Accuracy - All Images | 40.3 | #35 of 90 | Archive leaderboard | report |
| Domain Generalization | VizWiz-Classification | ResNet-50 (deepaugment+augmix) | Accuracy - Clean Images | 44.5 | #35 of 90 | Archive leaderboard | report |
| Domain Generalization | VizWiz-Classification | ResNet-50 (deepaugment+augmix) | Accuracy - Corrupted Images | 34.1 | #35 of 90 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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