GAT-GMM: Generative Adversarial Training for Gaussian Mixture Models

18 Jun 2020Farzan FarniaWilliam WangSubhro DasAli Jadbabaie

Generative adversarial networks (GANs) learn the distribution of observed samples through a zero-sum game between two machine players, a generator and a discriminator. While GANs achieve great success in learning the complex distribution of image, sound, and text data, they perform suboptimally in learning multi-modal distribution-learning benchmarks including Gaussian mixture models (GMMs)... (read more)

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