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

TL;DR

This paper introduces new datasets for real-world distribution shifts, evaluates existing robustness methods, and proposes a new data augmentation technique that improves out-of-distribution generalization across various shifts.

Contribution

It provides comprehensive benchmarks on real-world shifts, challenges prior claims about robustness methods, and introduces a novel augmentation that outperforms large-scale pretraining.

Findings

Larger models and data augmentations improve robustness.

Artificial robustness benchmarks can transfer to real-world shifts.

No single method consistently improves robustness across all shifts.

Abstract

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.