Bayesian GAN

NeurIPS 2017 Yunus SaatciAndrew G. Wilson

Generative adversarial networks (GANs) can implicitly learn rich distributions over images, audio, and data which are hard to model with an explicit likelihood. We present a practical Bayesian formulation for unsupervised and semi-supervised learning with GANs... (read more)

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