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GILBO: One Metric to Measure Them All
Alexander A. Alemi, Ian Fischer
Feb 12, 2018 (modified: Feb 12, 2018)ICLR 2018 Workshop Submissionreaders: everyone
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.
TL;DR:We introduce an information theoretic metric for evaluating GANs and VAEs.
Keywords:GAN, VAE, mutual information, metric
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