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Zhuang Qian1, Shufei Zhang2, Kaizhu Huang3
1Department of Electrical Engineering and Electronics, University of Liverpool, United Kingdom; School of Advanced Technology, Xi'an Jiaotong-Liverpool University, China.
这项研究引入了一种新的对抗性训练方法,该方法产生了多样化的对抗性示例,提高了深度神经网络的稳定性和概括性. 该方法通过创建更均的数据分布来缓解过度匹配,以加强对抗攻击的防御.
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