Approximation and Convergence Properties of Generative Adversarial Learning

NeurIPS 2017 Shuang LiuOlivier BousquetKamalika Chaudhuri

Generative adversarial networks (GAN) approximate a target data distribution by jointly optimizing an objective function through a "two-player game" between a generator and a discriminator. Despite their empirical success, however, two very basic questions on how well they can approximate the target distribution remain unanswered... (read more)

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