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Poster
GILBO: One Metric to Measure Them All
Alexander Alemi · Ian Fischer

Wed Dec 05 02:00 PM -- 04:00 PM (PST) @ Room 210 #57
in Wed Poster Session B »

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.

Keywords: Information Theory · Adversarial Networks · Generative Models · Latent Variable Models
[ Paper] 

Author Information

Alexander Alemi (Google)
Ian Fischer (Google)

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