  We present a scalable technique for upper bounding the Lipschitz constant of
generative models. We relate this quantity to the maximal norm over the set of
attainable vector-Jacobian products of a given generative model. We approximate
this set by layerwise convex approximations using zonotopes. Our approach
generalizes and improves upon prior work using zonotope transformers and we
extend to Lipschitz estimation of neural networks with large output dimension.
This provides efficient and tight bounds on small networks and can scale to
generative models on VAE and DCGAN architectures.
