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Jul, 2021
通过局部和全局潜在分布提高模型的鲁棒性
Improving Model Robustness with Latent Distribution Locally and Globally
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Zhuang Qian, Shufei Zhang, Kaizhu Huang, Qiufeng Wang, Rui Zhang...
TL;DR
通过全局流形的视角考虑深度神经网络对抗攻击的模型鲁棒性问题,提出了一种新的对抗训练方法ATLD,该方法在不受监督的情况下,利用了本地和全局潜在信息,通过对抗游戏生成潜在流形对抗性实例,保留了流形的局部和全局信息,具有良好的鲁棒性,实验结果表明该方法在多个数据集上显著优于现有技术。
Abstract
In this work, we consider
model robustness
of
deep neural networks
against
adversarial attacks
from a global manifold perspective. Leverag
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