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Feb, 2021
全局鲁棒神经网络
Globally-Robust Neural Networks
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Klas Leino, Zifan Wang, Matt Fredrikson
TL;DR
通过在网络中加入全局Lipschitz边界,文中提出的方法可以快速训练大型强健的神经网络,实现了可证明的最先进的可验证准确性。同时,该方法比最近的可证方法需要的时间和内存少得多,并在在线认证时产生可忽略的成本。
Abstract
The threat of
adversarial examples
has motivated work on training certifiably robust
neural networks
, to facilitate efficient verification of local robustness at inference time. We formalize a notion of
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