GILBO: One Metric to Measure Them All
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 VAEs each trained on four datasets (MNIST, FashionMNIST, CIFAR-10 and CelebA) and discuss the results.
Cite
Text
Alemi and Fischer. "GILBO: One Metric to Measure Them All." Neural Information Processing Systems, 2018.Markdown
[Alemi and Fischer. "GILBO: One Metric to Measure Them All." Neural Information Processing Systems, 2018.](https://mlanthology.org/neurips/2018/alemi2018neurips-gilbo/)BibTeX
@inproceedings{alemi2018neurips-gilbo,
title = {{GILBO: One Metric to Measure Them All}},
author = {Alemi, Alexander A and Fischer, Ian},
booktitle = {Neural Information Processing Systems},
year = {2018},
pages = {7037-7046},
url = {https://mlanthology.org/neurips/2018/alemi2018neurips-gilbo/}
}