Robust Few-Shot Learning with Adversarially Queried Meta-Learners

Micah Goldblum, Liam Fowl, Tom Goldstein

TL;DR: We develop meta-learning methods for adversarially robust few-shot learning.

Abstract: Previous work on adversarially robust neural networks requires large training sets and computationally expensive training procedures. On the other hand, few-shot learning methods are highly vulnerable to adversarial examples. The goal of our work is to produce networks which both perform well at few-shot tasks and are simultaneously robust to adversarial examples. We adapt adversarial training for meta-learning, we adapt robust architectural features to small networks for meta-learning, we test pre-processing defenses as an alternative to adversarial training for meta-learning, and we investigate the advantages of robust meta-learning over robust transfer-learning for few-shot tasks. This work provides a thorough analysis of adversarially robust methods in the context of meta-learning, and we lay the foundation for future work on defenses for few-shot tasks.

Keywords: meta-learning, adversarial, robust, few-shot

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:1910.00982/code)