Reviews: Adversarially Robust Generalization Requires More Data

The paper considered theoretical results on adversarially robust generalization, which studies the robustness of classifiers in the presence of even small noise. In particular, the work studied the generalization of adversarially robust learning by investigating the sample complexity in a comparison to that of standard learning. Specifically, the study focused on two simple concrete distribution models: gaussian model and Bernoulli model. For both models, the authors established the lower and upper bounds for the sample complexities. From these results, they drew the conclusion that the sample complexity of robust generalization is much larger than standard generalization.