ICML 2017poster177 citations
McGan: Mean and Covariance Feature Matching GAN
Youssef Mroueh, Tom Sercu, Vaibhava Goel
Abstract
We introduce new families of Integral Probability Metrics (IPM) for training Generative Adversarial Networks (GAN). Our IPMs are based on matching statistics of distributions embedded in a finite dimensional feature space. Mean and covariance feature matching IPMs allow for stable training of GANs, which we will call McGan. McGan minimizes a meaningful loss between distributions.
BibTeX
@InProceedings{pmlr-v70-mroueh17a,
title = {{M}c{G}an: Mean and Covariance Feature Matching {GAN}},
author = {Youssef Mroueh and Tom Sercu and Vaibhava Goel},
booktitle = {Proceedings of the 34th International Conference on Machine Learning},
pages = {2527--2535},
year = {2017},
editor = {Precup, Doina and Teh, Yee Whye},
volume = {70},
series = {Proceedings of Machine Learning Research},
month = {06--11 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v70/mroueh17a/mroueh17a.pdf},
url = {https://proceedings.mlr.press/v70/mroueh17a.html},
abstract = {We introduce new families of Integral Probability Metrics (IPM) for training Generative Adversarial Networks (GAN). Our IPMs are based on matching statistics of distributions embedded in a finite dimensional feature space. Mean and covariance feature matching IPMs allow for stable training of GANs, which we will call McGan. McGan minimizes a meaningful loss between distributions.}
}