  Generative adversarial networks (GANs) are very popular to generate realistic
images, but they often suffer from the training instability issues and the
phenomenon of mode loss. In order to attain greater diversity in GAN
synthesized data, it is critical to solving the problem of mode loss. Our work
explores probabilistic approaches to GAN modelling that could allow us to
tackle these issues. We present Prb-GANs, a new variation that uses dropout to
create a distribution over the network parameters with the posterior learnt
using variational inference. We describe theoretically and validate
experimentally using simple and complex datasets the benefits of such an
approach. We look into further improvements using the concept of uncertainty
measures. Through a set of further modifications to the loss functions for each
network of the GAN, we are able to get results that show the improvement of GAN
performance. Our methods are extremely simple and require very little
modification to existing GAN architecture.
