  We propose a simple, tractable lower bound on the mutual information
contained in the joint generative density of any latent variable generative
model: the GILBO (Generative Information Lower BOund). It offers a
data-independent measure of the complexity of the learned latent variable
description, giving the log of the effective description length. It is
well-defined for both VAEs and GANs. We compute the GILBO for 800 GANs and VAEs
each trained on four datasets (MNIST, FashionMNIST, CIFAR-10 and CelebA) and
discuss the results.
