在深度神经网络中,稀疏分布的表示对对抗性攻击的稳定性
Nida Sardar1, Sundas Khan1, Arend Hintze1,2
1Department for MicroData Analytics, Dalarna University, 791 88 Falun, Sweden.
Entropy (Basel, Switzerland)
|June 28, 2023
概括
放弃规范化增强神经网络对敌对攻击的弹性,但最佳结果取决于特定的放弃概率和最小化功能涂抹,神经元具有多个功能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 深度学习模型在任务中表现出色,但容易出现过度拟合和对抗性攻击.
- 放弃规范化是一种已知的技术,可以提高模型的概括性和稳定性.
研究的目的:
- 研究学规范化对神经网络对抗对抗攻击的强度的影响.
- 分析放弃规范化,功能涂抹和对抗性弹性之间的关系.
主要方法:
- 研究了不同学概率对神经网络性能的影响.
- 量化功能涂抹,定义为参与多个功能的神经元.
- 在不同的停机条件下评估网络对敌对攻击的抵抗力.
主要成果:
- 放弃规范化提高了在特定概率范围内对敌对攻击的抵抗力.
- 退学显著增加了各种退学率的功能涂抹.
- 功能涂抹较低的网络表现出更大的对抗性弹性.
结论:
- 退学规范化增强了对抗攻击的稳定性,但对退学概率敏感.
- 减少功能涂抹对于提高对抗性弹性至关重要,即使在学.
- 平衡放弃一般化和最小化功能涂抹是强大的深度学习模型的关键.
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