Estimating Neural Network Robustness via Lipschitz Constant and Architecture Sensitivity

TL;DR

This paper explores how the Lipschitz constant, derived from neural network architecture, can be used to estimate and improve the robustness of perception systems in robotic learning, especially against small perturbations.

Contribution

It introduces an analytical method to compute the Lipschitz constant from network architecture, linking design choices to robustness in perception neural networks.

Findings

Lipschitz constant correlates with network robustness.

Architectural modifications can reduce the Lipschitz constant.

Theoretical insights guide the design of safer perception systems.

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 investigates the robustness of neural networks in perception systems, specifically examining their sensitivity to targeted, small-scale perturbations. We identify the Lipschitz constant as a key metric for quantifying and enhancing network robustness. We derive an analytical expression to compute the Lipschitz constant based on neural network architecture, providing a theoretical basis for estimating and improving robustness. Several experiments reveal the relationship between network design, the Lipschitz constant, and robustness, offering practical insights for developing safer, more robust robot learning systems.