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

2019-10-02 · NeurIPS 2020 12 · Micah Goldblum, Liam Fowl, Tom Goldstein

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.

📄 PDF Abstract BibTeX arXiv:1910.00982

Code (1)

goldblum/AdversarialQuerying 공식 구현 pytorch

Tasks

Classification Few-Shot Image Classification Few-Shot Learning General Classification image-classification Image Classification Meta-Learning Transfer Learning

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Classification Few-Shot Image Classification image-classification Image Classification+1