Lipschitz Continuity in Deep Learning: A Systematic Review of Theoretical Foundations, Estimation Methods, Regularization Approaches, and Certifiable Robustness

Lipschitz continuity is a fundamental property of neural networks that characterizes their sensitivity to input perturbations.

It plays a pivotal role in deep learning, governing \textbf{robustness}, \textbf{generalization} and \textbf{optimization dynamics}.

Despite its importance, research on Lipschitz continuity is scattered across various domains, lacking a unified perspective.

This paper addresses this gap by providing a systematic review of Lipschitz continuity in deep learning.

We explore its \textbf{theoretical foundations}, \textbf{estimation methods}, \textbf{regularization approaches}, and \textbf{certifiable robustness}.

By reviewing existing research through the lens of Lipschitz continuity, this survey serves as a comprehensive reference for researchers and practitioners seeking a deeper understanding of Lipschitz continuity and its implications in deep learning.