Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
2017
Conference Paper
avg
Variational Autoencoders (VAEs) are expressive latent variable models that can be used to learn complex probability distributions from training data. However, the quality of the resulting model crucially relies on the expressiveness of the inference model. We introduce Adversarial Variational Bayes (AVB), a technique for training Variational Autoencoders with arbitrarily expressive inference models. We achieve this by introducing an auxiliary discriminative network that allows to rephrase the maximum-likelihood-problem as a two-player game, hence establishing a principled connection between VAEs and Generative Adversarial Networks (GANs). We show that in the nonparametric limit our method yields an exact maximum-likelihood assignment for the parameters of the generative model, as well as the exact posterior distribution over the latent variables given an observation. Contrary to competing approaches which combine VAEs with GANs, our approach has a clear theoretical justification, retains most advantages of standard Variational Autoencoders and is easy to implement.
Author(s): | L. Mescheder and S. Nowozin and A. Geiger |
Book Title: | Proceedings of the 34th International Conference on Machine Learning |
Volume: | 70 |
Year: | 2017 |
Month: | August |
Day: | 6-11 |
Series: | Proceedings of Machine Learning Research |
Editors: | Doina Precup, Yee Whye Teh |
Publisher: | PMLR |
Department(s): | Autonomous Vision |
Research Project(s): |
Convergence and Stability of GAN training
|
Bibtex Type: | Conference Paper (inproceedings) |
Paper Type: | Conference |
Event Name: | International Conference on Machine Learning (ICML) |
Event Place: | International Convention Centre, Sydney, Australia |
ISSN: | 1938-7228 |
Links: |
pdf
suppmat Project Page arxiv-version |
BibTex @inproceedings{Mescheder2017ICML, title = {Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks}, author = {Mescheder, L. and Nowozin, S. and Geiger, A.}, booktitle = {Proceedings of the 34th International Conference on Machine Learning}, volume = {70}, series = {Proceedings of Machine Learning Research}, editors = {Doina Precup, Yee Whye Teh}, publisher = {PMLR}, month = aug, year = {2017}, doi = {}, month_numeric = {8} } |