Robust Few-Shot Learning with Adversarially Queried Meta-Learners

ICLR 2020 Anonymous

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... (read more)

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