  In recent years, Generative Adversarial Networks (GANs) have received
significant attention from the research community. With a straightforward
implementation and outstanding results, GANs have been used for numerous
applications. Despite the success, GANs lack a proper theoretical explanation.
These models suffer from issues like mode collapse, non-convergence, and
instability during training. To address these issues, researchers have proposed
theoretically rigorous frameworks inspired by varied fields of Game theory,
Statistical theory, Dynamical systems, etc.
  In this paper, we propose to give an appropriate structure to study these
contributions systematically. We essentially categorize the papers based on the
issues they raise and the kind of novelty they introduce to address them.
Besides, we provide insight into how each of the discussed articles solves the
concerned problems. We compare and contrast different results and put forth a
summary of theoretical contributions about GANs with focus on image/visual
applications. We expect this summary paper to give a bird's eye view to a
person wishing to understand the theoretical progress in GANs so far.
