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

arXiv: https://arxiv.org/abs/1910.00982 · HTML: https://mlsanity.com/paper/1910.00982/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2019.

## Built on by (most cited first, 6 of 6)

- [Robust Fast Adaptation from Adversarially Explicit Task Distribution Generation](https://mlsanity.com/paper/2407.19523/) (2024)
- [A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm](https://mlsanity.com/paper/2310.00708/) (2023)
- [A Simple Approach to Adversarial Robustness in Few-shot Image Classification](https://mlsanity.com/paper/2204.05432/) (2022)
- [Robust Generalization with Adaptive Optimal Transport Priors for Decision-Focused Learning](https://mlsanity.com/paper/2602.01427/) (2026)
- [Adaptive Adversarial Training for Meta Reinforcement Learning](https://mlsanity.com/paper/2104.13302/) (2021)
- [AutoProtoNet: Interpretability for Prototypical Networks](https://mlsanity.com/paper/2204.00929/) (2022)

## Abstract (arXiv)

Previous work on adversarially robust neural networks for image classification 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 classification tasks and are simultaneously robust to adversarial examples. We develop an algorithm, called Adversarial Querying (AQ), for producing adversarially robust meta-learners, and we thoroughly investigate the causes for adversarial vulnerability. Moreover, our method achieves far superior robust performance on few-shot image classification tasks, such as Mini-ImageNet and CIFAR-FS, than robust transfer learning.