BriefGPT.xyz
Mar, 2018
随机激活剪枝的鲁棒性对抗防御
Stochastic Activation Pruning for Robust Adversarial Defense
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Guneet S. Dhillon, Kamyar Azizzadenesheli, Zachary C. Lipton, Jeremy Bernstein, Jean Kossaifi...
TL;DR
通过开发深度神经网络中的混合策略,并将其形式化为博弈论的最小值零和博弈,我们提出了随机激活修剪(Stochastic Activation Pruning,SAP),并证明它能够提高深度学习系统对抗性攻击的鲁棒性。
Abstract
neural networks
are known to be vulnerable to
adversarial examples
. Carefully chosen perturbations to real images, while imperceptible to humans, induce misclassification and threaten the reliability of deep lear
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