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Oct, 2023
广泛深度神经网络的鲁棒过拟合的理论分析:一种NTK方法
Theoretical Analysis of Robust Overfitting for Wide DNNs: An NTK Approach
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Shaopeng Fu, Di Wang
TL;DR
深度神经网络的对抗训练存在鲁棒过拟合问题,本文通过理论分析提出了神经切线核理论在对抗训练中的扩展,并证明了经过对抗训练的宽深度神经网络可以很好近似为一个线性化的神经网络;进一步,设计了用于无限宽度深度神经网络的对抗训练算法 Adv-NTK,实验证明 Adv-NTK 能够使无限宽度深度神经网络提高鲁棒性。
Abstract
adversarial training
(AT) is a canonical method for enhancing the robustness of deep neural networks (DNNs). However, recent studies empirically demonstrated that it suffers from
robust overfitting
, i.e., a long
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