相关实验视频
霍尔德网络用于提高对抗性稳定性
Dazhi Zhao1, Haiyan Li2, Qin Luo2
1School of Sciences, Southwest Petroleum University, Chengdu, 610500, China; Institute for Artificial Intelligence, Southwest Petroleum University, Chengdu, 610500, China.
概括
拟议的霍尔德网络,使用alpha-rectified功率单元 (α-RePU),通过强制执行alpha-Hölder连续性,提供了更好的对抗性稳定性和概括性,优于传统的利普希茨受约束模型.
科学领域:
- 机器学习 机器学习
- 深度学习架构 深度学习架构
- 人工智能中的稳定性
背景情况:
- 利普希茨常数增强模型的稳定性,但可以限制函数近似能力.
- 现有的方法可能会过度限制神经网络,阻碍复杂任务的执行.
研究的目的:
- 介绍霍尔德网络,这是一个概括利普希茨约束的新型架构.
- 使用α-修正功率单元 (α-RePU) 强制执行alpha-Hölder连续性.
- 为霍尔德连续函数的普遍近似提供理论保证.
主要方法:
- 开发了包含α-RePU的霍尔德网络架构.
- 从理论上证明了霍尔德连续函数的普遍近似属性.
- 对图像分类和表格数据进行经验评估.
主要成果:
- 霍尔德网络的准确性与现有模型相提并论.
- 证明了对各种攻击 (例如,PGD,l∞) 的优越对抗性强度.
- 展示了超越严格的利普希茨约束的灵活性.
结论:
- 霍尔德网络提供了一种更灵活,更强大的替代标准的利普希茨限制网络.
- α-RePU 单元有效地实现了 α-Hölder 连续性,提高了模型的概括性和稳定性.
- 拟议的框架显示了改善人工智能模型在各种应用中的弹性.
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