  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 $y'$ 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.
