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In recent years, Generative Adversarial Networks (GANs) have receivedsignificant attention from the research community. With a straightforwardimplementation and outstanding results, GANs have been used for numerousapplications. Despite the success, GANs lack a proper theoretical explanation.These models suffer from issues like mode collapse, non-convergence, andinstability during training. To address these issues, researchers have proposedtheoretically 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 thesecontributions systematically. We essentially categorize the papers based on theissues they raise and the kind of novelty they introduce to address them.Besides, we provide insight into how each of the discussed articles solves theconcerned problems. We compare and contrast different results and put forth asummary of theoretical contributions about GANs with focus on image/visualapplications. We expect this summary paper to give a bird's eye view to aperson wishing to understand the theoretical progress in GANs so far.

Generative Adversarial Networks (GANs): What it can generate and What it cannot?