Reviews: Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty

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.