Smoothed Embeddings for Certified Few-Shot Learning

• URL: http://arxiv.org/abs/2202.01186v1
• Date: Wed, 2 Feb 2022 18:19:04 GMT
• Title: Smoothed Embeddings for Certified Few-Shot Learning
• Authors: Mikhail Pautov, Olesya Kuznetsova, Nurislam Tursynbek, Aleksandr Petiushko, Ivan Oseledets
• Abstract summary: We extend randomized smoothing to few-shot learning models that map inputs to normalized embeddings. Our results are confirmed by experiments on different datasets.
• Score: 63.68667303948808
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Randomized smoothing is considered to be the state-of-the-art provable defense against adversarial perturbations. However, it heavily exploits the fact that classifiers map input objects to class probabilities and do not focus on the ones that learn a metric space in which classification is performed by computing distances to embeddings of classes prototypes. In this work, we extend randomized smoothing to few-shot learning models that map inputs to normalized embeddings. We provide analysis of Lipschitz continuity of such models and derive robustness certificate against $\ell_2$-bounded perturbations that may be useful in few-shot learning scenarios. Our theoretical results are confirmed by experiments on different datasets.

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