  Generative Adversarial Network (GAN) is a current focal point of research.
The body of knowledge is fragmented, leading to a trial-error method while
selecting an appropriate GAN for a given scenario. We provide a comprehensive
summary of the evolution of GANs starting from its inception addressing issues
like mode collapse, vanishing gradient, unstable training and non-convergence.
We also provide a comparison of various GANs from the application point of
view, its behaviour and implementation details. We propose a novel framework to
identify candidate GANs for a specific use case based on architecture, loss,
regularization and divergence. We also discuss application of the framework
using an example, and we demonstrate a significant reduction in search space.
This efficient way to determine potential GANs lowers unit economics of AI
development for organizations.
