Reviews: Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks

I feel that both the precise training algorithm as well as the distinction of the two types of prototypes are important points to add to the revised version. I agree with the other reviewers that Section 3 can be written in a more clear way, and it would also be helpful to double-check the text for grammar for the final version. The problem of low-shot learning is to learn classifiers between a set of previously-unseen classes given only a few examples of each. It is assumed that a potentially large set of'training' or'base' classes is given, but this set is disjoint from the set of classes that will be presented at test time. Given the insufficiency of the training data for learning a low-shot classifier, generating additional data is a reasonable strategy and there has been increasingly more research on this recently (the authors correctly cite the relevant works).