Principles of Lipschitz continuity in neural networks

TL;DR

This paper explores the fundamental principles of Lipschitz continuity in neural networks, examining its role in robustness and generalization from both training dynamics and feature modulation perspectives.

Contribution

It provides a theoretical analysis of Lipschitz continuity in neural networks, focusing on its evolution during training and its influence on frequency signal propagation.

Findings

Lipschitz continuity evolves during training, affecting robustness.

Lipschitz constraints influence frequency signal propagation.

Theoretical insights into the role of Lipschitz continuity in neural network behavior.

Abstract

Deep learning has achieved remarkable success across a wide range of domains, significantly expanding the frontiers of what is achievable in artificial intelligence. Yet, despite these advances, critical challenges remain -- most notably, ensuring robustness to small input perturbations and generalization to out-of-distribution data. These critical challenges underscore the need to understand the underlying fundamental principles that govern robustness and generalization. Among the theoretical tools available, Lipschitz continuity plays a pivotal role in governing the fundamental properties of neural networks related to robustness and generalization. It quantifies the worst-case sensitivity of network's outputs to small input perturbations. While its importance is widely acknowledged, prior research has predominantly focused on empirical regularization approaches based on Lipschitz constraints, leaving the underlying principles less explored. This thesis seeks to advance a principled understanding of the principles of Lipschitz continuity in neural networks within the paradigm of machine learning, examined from two complementary perspectives: an internal perspective -- focusing on the temporal evolution of Lipschitz continuity in neural networks during training (i.e., training dynamics); and an external perspective -- investigating how Lipschitz continuity modulates the behavior of neural networks with respect to features in the input data, particularly its role in governing frequency signal propagation (i.e., modulation of frequency signal propagation).