  Generative Adversarial Networks (GANs) have recently attracted considerable
attention in the AI community due to its ability to generate high-quality data
of significant statistical resemblance to real data. Fundamentally, GAN is a
game between two neural networks trained in an adversarial manner to reach a
zero-sum Nash equilibrium profile. Despite the improvement accomplished in GANs
in the last few years, several issues remain to be solved. This paper reviews
the literature on the game theoretic aspects of GANs and addresses how game
theory models can address specific challenges of generative model and improve
the GAN's performance. We first present some preliminaries, including the basic
GAN model and some game theory background. We then present taxonomy to classify
state-of-the-art solutions into three main categories: modified game models,
modified architectures, and modified learning methods. The classification is
based on modifications made to the basic GAN model by proposed game-theoretic
approaches in the literature. We then explore the objectives of each category
and discuss recent works in each category. Finally, we discuss the remaining
challenges in this field and present future research directions.
