Generative Adversarial Networks: zero-sum game in game theory

Generative Adversarial Networks (GAN) has recently received considerable attention in the intelligence community because of their ability to generate high quality and significant data. GAN is a game between two players where one player’s loss is the gain of another and that is a way to reach Nash that is balanced by the sum of zero. Despite these networks over the years, this paper examines the theoretical aspects of the game in GAN and how it plays. Then the research discusses the type of game in these networks. Later, after examining the challenges of this network, it will be implemented while maintaining equilibrium.

Keywords:

Generative Adversarial Network (GAN) , Game Theory , Zero-sum Game , Nash Equilibrium , Deep Learning