Generative Adversarial Network, as a promising research direction in the AI community, recently attracts considerable attention due to its ability to generating high-quality realistic data. GANs are a competing game between two neural networks trained in an adversarial manner to reach a Nash equilibrium. Despite the improvement accomplished in GANs in the last years, there remain several issues to solve. In this way, how to tackle these issues and make advances leads to rising research interests. This paper reviews literature that leverages the game theory in GANs and addresses how game models can relieve specific generative models' challenges and improve the GAN's performance. In particular, we firstly review some preliminaries, including the basic GAN model and some game theory backgrounds. After that, we present our taxonomy to summarize the state-of-the-art solutions into three significant categories: modified game model, modified architecture, and modified learning method. The classification is based on the modifications made in the basic model by the proposed approaches from the game-theoretic perspective. We further classify each category into several subcategories. Following the proposed taxonomy, we explore the main objective of each class and review the recent work in each group. Finally, we discuss the remaining challenges in this field and present the potential future research topics.

中文翻译：

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GAN 游戏：生成对抗网络的博弈论模型

生成对抗网络作为人工智能界一个很有前途的研究方向，最近因其生成高质量真实数据的能力而受到广泛关注。GAN 是两个以对抗方式训练以达到纳什均衡的神经网络之间的竞争游戏。尽管过去几年 GAN 取得了进步，但仍有几个问题需要解决。通过这种方式，如何解决这些问题并取得进展导致研究兴趣上升。本文回顾了在 GAN 中利用博弈论的文献，并讨论了博弈模型如何减轻特定生成模型的挑战并提高 GAN 的性能。特别是，我们首先回顾了一些预备知识，包括基本的 GAN 模型和一些博弈论背景。之后，我们提出了我们的分类法，将最先进的解决方案总结为三个重要类别：修改后的游戏模型、修改后的架构和修改后的学习方法。分类基于从博弈论角度提出的方法对基本模型所做的修改。我们进一步将每个类别分为几个子类别。按照提议的分类法，我们探索每个类的主要目标，并回顾每个组最近的工作。最后，我们讨论了该领域的剩余挑战，并提出了未来潜在的研究课题。分类基于从博弈论角度提出的方法对基本模型所做的修改。我们进一步将每个类别分为几个子类别。按照提议的分类法，我们探索每个类的主要目标，并回顾每个组最近的工作。最后，我们讨论了该领域的剩余挑战，并提出了未来潜在的研究课题。分类基于从博弈论角度提出的方法对基本模型所做的修改。我们进一步将每个类别分为几个子类别。按照提议的分类法，我们探索每个类的主要目标，并回顾每个组最近的工作。最后，我们讨论了该领域的剩余挑战，并提出了未来潜在的研究课题。