From: ICML.cc/2021/Workshop/AML/Program_Chairs
title: Paper Decision
decision: Accept (Poster)
comment: This paper proposed a robust meta-learning method for few-shot classification. The performance is better and the results are promising.

From: ICML.cc/2021/Workshop/AML/Paper3/Reviewer_Vez6
title: The proposed method is promising. More theoretical analysis should be given. Accept.
review: This paper proposed a meta-learning method on the adversarially robust neural network. The method is called Long-term Cross Adversarial Training (LCAT). The proposed LCAT will update metalearning model parameters cross along the natural and adversarial sample distribution direction with long-term to improve both adversarial and clean few-shot classification accuracy. 

Pros: The proposed method is computationally expensive. The performance is better than SOTA. Experiment results are promising.

Cons: More theoretical analysis should be given about the proposed method.
rating: Accept
confidence: 4: The reviewer is confident but not absolutely certain that the evaluation is correct