Bayesian Conditional Generative Adverserial Networks.

Traditional GANs use a deterministic generator function (typically a neural network) to transform a random noise input $z$ to a sample $mathbf{x}$ that the discriminator seeks to distinguish. We propose a new GAN called Bayesian Conditional Generative Adversarial Networks (BC-GANs) that use a random generator function to transform a deterministic input $yu0027$ to a sample $mathbf{x}$. Our BC-GANs extend traditional GANs to a Bayesian framework, and naturally handle unsupervised learning, supervised learning, and semi-supervised learning problems. Experiments show that the proposed BC-GANs outperforms the state-of-the-arts.