Games of GANs: game-theoretical models for generative adversarial networks

Monireh Mohebbi Moghaddam, Bahar Boroomand, Mohammad Jalali, Arman Zareian, Alireza Daeijavad, Mohammad Hossein Manshaei, Marwan Krunz

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Generative Adversarial Networks (GANs) have recently attracted considerable attention in the AI community due to their 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 models and improve the GAN’s performance. We first present some preliminaries, including the basic GAN model and some game theory background. We then present a 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 class. Finally, we discuss the remaining challenges in this field and present future research directions.

Original languageEnglish (US)
Pages (from-to)9771-9807
Number of pages37
JournalArtificial Intelligence Review
Issue number9
StatePublished - Sep 2023


  • Deep generative models
  • Deep learning
  • Game theory
  • Generative adversarial network (GAN)
  • Multi-agent systems

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Artificial Intelligence


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