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# Adversarially Robust Few-Shot Learning: A Meta-Learning Approach

## Motivation

FSL methods are highly vulnerable to adversarial examples. 

The goal of this paper is to produce NN which both perform well at few-shot classification tasks and simultaneously robust to adversarial examples.

## Reference

* <https://arxiv.org/pdf/1910.00982.pdf>