mlsanityData to 7 Oct 2026

Game of GANs: Game-Theoretical Models for Generative Adversarial Networks

arXiv 2106.06976 ↗First posted 13 Jun 2021cs.LG cs.AI cs.GTcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 50 of its 94 references that are in the map. First posted on arXiv 13 Jun 2021.

Builds on

50 of 94 references.

References · 50

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38 more
  1. 2020Adversarial Continual Learningcited by 140
  2. 2017Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Modelscited by 116
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Cited by year

Citing papers per year: 2023: 2; 2024: 2; 2025: 2.

6 papers.

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  2. 2024Rethinking Teacher-Student Curriculum Learning through the Cooperative Mechanics of Experiencecited by 1
  3. 2025QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learningcited by 1
  4. 2023ApproxED: Approximate exploitability descent via learned best responsescited by 0
  5. 2024The Bid Picture: Auction-Inspired Multi-player Generative Adversarial Networks Trainingcited by 0
  6. 2025Nested Annealed Training Scheme for Generative Adversarial Networkscited by 0