McGan: Mean and Covariance Feature Matching GAN

Mroueh, Youssef, Sercu, Tom, Goel, Vaibhava

arXiv.org Machine Learning 

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 Mc-Gan.

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