Defensive Few-shot Learning

Understand

This paper investigates a new challenging problem called defensive few-shot learning in order to learn a robust few-shot model against adversarial attacks.

• Simply applying the existing adversarial defense methods to few-shot learning cannot effectively solve this problem.
• This is because the commonly assumed sample-level distribution consistency between the training and test sets can no longer be met in the few-shot setting.
• To address this situation, we develop a general defensive few-shot learning (DFSL) framework to answer the following two key questions: (1) how to transfer adversarial defense knowledge from one sample distribution to another? (2) how to narrow the distribution gap between clean and adversarial examples under the few-shot setting? To answer the first question, we propose an episode-based adversarial training mechanism by assuming a task-level distribution consistency to better transfer the adversarial defense knowledge.