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  • title: Smoothed Embeddings for Certified Few-Shot Learning
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            Smoothed Embeddings for Certified Few-Shot Learning
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            Smoothed Embeddings for Certified Few-Shot Learning

            Nov 28, 2022

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            Mikhail Pautov

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            Olesya Kuznetsova

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            NT

            Nurislam Tursynbek

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            About

            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…

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            NeurIPS 2022

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