# Adversarially Robust Few-Shot Learning: A Meta-Learning Approach Source: [Adversarially Robust Few-Shot Learning: A Meta-Learning Approach]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:38:59.898Z Authors: Micah Goldblum, Liam Fowl, Tom Goldstein Published: 2019-10-02T14:39:21.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()