McGan: Mean and Covariance Feature Matching GAN
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:2527-2535, 2017.
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.