对抗性培训的数据依赖稳定性分析
Yihan Wang1, Shuang Liu1, Xiao-Shan Gao1
1Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 101408, China.
概括
这项研究为深度学习中的对抗性训练引入了新的概括界限,包括数据分布. 这些边界提高了对强有力的概括和分布变化的影响的理解.
科学领域:
- 深度学习 (Deep Learning) 是一种深度学习.
- 机器学习理论机器学习理论
- 人工智能中的稳定性
背景情况:
- 稳定性分析对于深度学习的一般化至关重要.
- 敌对训练是对抗攻击的关键防御.
- 现有的概括界限缺乏数据分布信息.
研究的目的:
- 为包括数据分布在内的对抗性训练提供概括界限.
- 分析数据分布和对抗预算对概括差距的影响.
- 提高对深度学习中强大的概括的理解.
主要方法:
- 使用平均稳定性和高阶近似的利普希茨条件.
- 导出对凸和非凸损失的概括界限.
- 分析分配转移和对抗预算的影响.
主要成果:
- 开发了新的概括界限,包括用于对抗训练的数据分布.
- 边界与现有的基于稳定性的统一边界相似或更高.
- 证明了分布从数据中毒转移到强大的概括的影响.
结论:
- 拟议的概括界限为对抗训练的强度提供了更深入的见解.
- 数据分布在对抗性环境中显著影响了概括差距.
- 这些发现对于开发针对各种攻击的更有弹性的深度学习模型至关重要.
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