  Generative adversarial networks (GANs) have shown remarkable success in
generation of unstructured data, such as, natural images. However, discovery
and separation of modes in the generated space, essential for several tasks
beyond naive data generation, is still a challenge. In this paper, we address
the problem of imposing desired modal properties on the generated space using a
latent distribution, engineered in accordance with the modal properties of the
true data distribution. This is achieved by training a latent space inversion
network in tandem with the generative network using a divergence loss. The
latent space is made to follow a continuous multimodal distribution generated
by reparameterization of a pair of continuous and discrete random variables. In
addition, the modal priors of the latent distribution are learned to match with
the true data distribution using minimal-supervision with negligible increment
in number of learnable parameters. We validate our method on multiple tasks
such as mode separation, conditional generation, and attribute discovery on
multiple real world image datasets and demonstrate its efficacy over other
state-of-the-art methods.
