Review for NeurIPS paper: Adversarially Robust Few-Shot Learning: A Meta-Learning Approach

Weaknesses: [Edit after Author Response] I thank the authors for acknowledging the suggestion for merging the tables, captioning and moving Algorithm 1 to the Appendix. The author response does not elaborate or explain too much on this, but rather states the observations from Table 8. 2) While I thank the authors for performing more experiments on state of the art meta-learning approaches like MCT and mentioning that AQ on MCT reduces the drop of natural accuracy, the current results in the paper using other meta-learning approaches do have a large drop in the natural accuracy. This certainly diminishes the practical use of AQ. ------- I agree that the paper does have some good positive points. However I am slightly inclined towards a rejection currently primarily due to the following reasons: 1) The core idea of this paper is very simple and straightforward. Though the authors justify that they are the first to do it, I am unsure whether this work might count as a novel enough contribution for the NeurIPS community. In contrast, from results in Table 4,5 vs Table 2, it appears that using AQ causes a big drop (sometimes almost 15-20%) in the natural accuracy.