  Generative adversarial networks (GANs) while being very versatile in
realistic image synthesis, still are sensitive to the input distribution. Given
a set of data that has an imbalance in the distribution, the networks are
susceptible to missing modes and not capturing the data distribution. While
various methods have been tried to improve training of GANs, these have not
addressed the challenges of covering the full data distribution. Specifically,
a generator is not penalized for missing a mode. We show that these are
therefore still susceptible to not capturing the full data distribution.
  In this paper, we propose a simple approach that combines an encoder based
objective with novel loss functions for generator and discriminator that
improves the solution in terms of capturing missing modes. We validate that the
proposed method results in substantial improvements through its detailed
analysis on toy and real datasets. The quantitative and qualitative results
demonstrate that the proposed method improves the solution for the problem of
missing modes and improves training of GANs.
