Robust Few-Shot Learning with Adversarially Queried Meta-Learners

By Micah Goldblum, Liam H. Fowl, Tom Goldstein. 2019

14 citations recorded by the source corpus.

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. The goal of our work is to produce networks which both perform well at few-shot tasks and are simultaneously robust to adversarial examples. We adapt adversarial training for meta-learning, we adapt robust architectural features to small networks for meta-learning, we test pre-processing defenses as an alternative to adversarial training for meta-learning, and we investigate the advantages of robust meta-learning over robust transfer-learning for few-shot tasks. This work provides a thorough analysis of adversarially robust methods in the context of meta-learning, and we lay the foundation for future work on defenses for few-shot tasks.

Subfields: Artificial Intelligence

Citations in the corpus

• Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks (2020, neutral): “A formal description of this process can be found in Algorithm 1, as seen in (Goldblum et al., 2019).”