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

NeurIPS 20 11/9/2020

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