- Keywords: GAN, VAE, mutual information, metric
- TL;DR: We introduce an information theoretic metric for evaluating GANs and VAEs.
- Abstract: 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 VAE s trained on MNIST and discuss the results.