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.

The authors present a way of self-supervised auxiliary learning in which the images in the training set are rotated with 4 different rotations, and the neural network has to predict the type of rotation. The authors show with various experiments that this type of SSL increases the robustness against all kinds of perturbations, ranging from adversarial attacks to motion blur and fog. In addition, the outputs indicating the rotation can be used for detecting outliers. The article makes a good case for both contributions. One main remark is that the title of the article talks about uncertainty estimation, while the experiments focus on outlier detection. These two tasks are related but not identical.

This paper received mixed reviews. All reviewers found the empirical findings in the paper to be very interesting. The main concern from reviewers was about the lack of theoretical justification for the findings. However, many empirical results precede theoretical results, and this paper's empirical results are interesting in its own right. The area chair has read the paper in detail. The paper is well written, and provides important empirical analysis for two timely questions in the field today: model robustness and self-supervised learning.

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.

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. Papers published at the Neural Information Processing Systems Conference.