BriefGPT.xyz
Nov, 2015
对抗样本的统一梯度正则化方法族
A Unified Gradient Regularization Family for Adversarial Examples
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Chunchuan Lyu, Kaizhu Huang, Hai-Ning Liang
TL;DR
本文提出了一种统一框架来构建抵御对抗样本的强大机器学习模型,并通过梯度正则化方法有效地对代价函数的梯度进行惩罚,从而达到鲁棒性的目的。实验证明,该方法在两个基准数据集上达到了最佳精度。
Abstract
adversarial examples
are augmented data points generated by imperceptible perturbation of input samples. They have recently drawn much attention with the
machine learning
and data mining community. Being difficul
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