Lipschitz Certificates for Neural Network Structures Driven by Averaged Activation Operators

This work derives sharp Lipschitz constants for feed-forward neural networks in the context of a general layered network model involving compositions of nonexpansive averaged operators and affine operators, which finely captures the interactions between the layers, yielding tighter LipsChitz constants than those resulting from the product of individual bounds for groups of layers.