Venue: ICML 2021 Workshop AML

Reviews:

final_score: rating: Accept
review_content: 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.
review_title: The proposed method is promising. More theoretical analysis should be given. Accept.

Meta-review: This paper proposed a robust meta-learning method for few-shot classification. The performance is better and the results are promising. 
Decision: Accept (Poster)