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[R] Improving Adversarial Robustness via Guided Complement Entropy (ICCV’19)

TL;DR: We propose a new training paradigm called Guided Complement Entropy (GCE) that is capable of achieving “adversarial defense for free,” which involves no additional procedures in the process of improving adversarial robustness. In addition to maximizing model probabilities on the ground-truth class like cross-entropy, we neutralize its probabilities on the incorrect classes along with a “guided” term to balance between these two terms. We show in the experiments that our method achieves better model robustness with even better performance compared to the commonly used cross-entropy training objective.

Full paper: https://arxiv.org/abs/1903.09799

Github: https://github.com/henry8527/GCE

submitted by /u/henry8527
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.