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Oct, 2024
通过利普希茨常数和架构敏感性估计神经网络鲁棒性
Estimating Neural Network Robustness via Lipschitz Constant and Architecture Sensitivity
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Abulikemu Abuduweili, Changliu Liu
TL;DR
本研究解决了神经网络在感知系统中对小规模干扰的敏感性问题，提出了使用利普希茨常数作为量化和增强网络鲁棒性的关键指标。通过分析网络架构与鲁棒性之间的关系，提供了理论基础和实践洞见，为开发更安全、更鲁棒的机器人学习系统奠定了基础。
Abstract
Ensuring neural network
Robustness
is essential for the safe and reliable operation of
Robotic Learning
systems, especially in perception and decision-making tasks within real-world environments. This paper inves
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