McGan: Mean and Covariance Feature Matching GAN

Youssef Mroueh; Tom Sercu; Vaibhava Goel

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arXiv · 1702.08398

McGan: Mean and Covariance Feature Matching GAN

Youssef Mroueh·Tom Sercu·Vaibhava Goel

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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.

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Youssef Mroueh, Tom Sercu, Vaibhava Goel. 2017-02-27. McGan: Mean and Covariance Feature Matching GAN. https://arxiv.org/abs/1702.08398

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