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  • title: Online Adversarial Purification based on Self-supervised Learning
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            Online Adversarial Purification based on Self-supervised Learning
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            Online Adversarial Purification based on Self-supervised Learning

            May 3, 2021

            Speakers

            CS

            Changhao Shi

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            CH

            Chester Holtz

            Speaker · 0 followers

            GM

            Gal Mishne

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            About

            Deep neural networks are known to be vulnerable to adversarial examples, where a perturbation in the input space leads to an amplified shift in the latent network representation. In this paper, we combine canonical supervised learning with self-supervised representation learning, and present Self-supervised Online Adversarial Purification (SOAP), a novel defense strategy that uses a self-supervised loss to purify adversarial examples at test-time. Our approach leverages the label-independent nat…

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            ICLR 2021

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            About ICLR 2021

            The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning. ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, text understanding, gaming, and robotics.

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