  Self-supervision provides effective representations for downstream tasks
without requiring labels. However, existing approaches lag behind fully
supervised training and are often not thought beneficial beyond obviating or
reducing the need for annotations. We find that self-supervision can benefit
robustness in a variety of ways, including robustness to adversarial examples,
label corruption, and common input corruptions. Additionally, self-supervision
greatly benefits out-of-distribution detection on difficult, near-distribution
outliers, so much so that it exceeds the performance of fully supervised
methods. These results demonstrate the promise of self-supervision for
improving robustness and uncertainty estimation and establish these tasks as
new axes of evaluation for future self-supervised learning research.

Machine Learning;Computer Vision and Pattern Recognition;Machine Learning