A Novel Framework for Selection of GANs for an Application

Understand

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.