BriefGPT.xyz
Apr, 2018
对抗鲁棒性泛化需要更多数据
Adversarially Robust Generalization Requires More Data
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Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Mądry
TL;DR
本文研究在简单自然数据模型中,对抗鲁棒学习的样本复杂度可以显著大于标准学习,这个差距是信息理论的,且与训练算法或模型家族无关。作者做了一些实验来证实这个结果。我们可以假设训练鲁棒分类器的困难,至少部分来自这种固有的更大的样本复杂度。
Abstract
machine learning
models are often susceptible to
adversarial perturbations
of their inputs. Even small perturbations can cause state-of-the-art classifiers with high "standard" accuracy to produce an incorrect pr
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