Name
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, ciEAR-10 and CelebA) and discuss the results. |
Year | Venue | Keywords |
|---|---|---|
2018 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018) | generative model,mutual information,latent variable,parallel optimization |
DocType | Volume | ISSN |
Conference | 31 | 1049-5258 |
Citations | PageRank | References |
1 | 0.35 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexander A. Alemi | 1 | 70 | 9.92 |
| Ian Fischer | 2 | 422 | 26.82 |