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McGan: Mean and Covariance Feature Matching GAN
February 27, 2017 · Declared Dead · 🏛 International Conference on Machine Learning
"No code URL or promise found in abstract"
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Authors
Youssef Mroueh, Tom Sercu, Vaibhava Goel
arXiv ID
1702.08398
Category
cs.LG: Machine Learning
Cross-listed
stat.ML
Citations
163
Venue
International Conference on Machine Learning
Last Checked
3 months ago
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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