Table 4.

A summary of critical approaches based on game-theoretic models to develop Generative Adversarial Network models

Generative Adversarial Network models	Game-theoretic models/concepts used	References
Deep learning model	Two-person sequential non-cooperative Stackelberg game, Stochastic payoff functions	[16]
GAN	Two-player zero-sum game	[41]
	Mixed Nash equilibrium	[56]
	Minimax game model	[129]
	Boundary equilibrium	[9]
	Local Nash equilibrium	[98]
	Nash equilibrium	[29]
GANG	A finitely long zero-sum game, Resource-bounded best responses (RBBRs), source bounded Nash Equilibrium (RB-NE)	[97]
Triple-GAN	Two-player games	[18]
GraphGAN	Minimax game	[133]
GAP	Minimax game	[58]
MIX+GAN	Approximate pure equilibrium	[5]
GraphSGAN	Expected equilibrium	[25]