McGan: Mean and Covariance Feature Matching GAN

Youssef Mroueh , Tom Sercu , Vaibhava Goel

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classification 💻 cs.LG stat.ML

keywords featurematchingmcgancovariancedistributionsipmsmeantraining

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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.

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Demystifying MMD GANs
stat.ML 2018-01 accept novelty 6.0

MMD GANs have unbiased critic gradients but biased generator gradients from sample-based learning, and the Kernel Inception Distance provides a practical new measure for GAN convergence and dynamic learning rate adaptation.