A Survey on Generative Adversarial Networks: Variants, Applications, and Training

Understand

The Generative Models have gained considerable attention in the field of unsupervised learning via a new and practical framework called Generative Adversarial Networks (GAN) due to its outstanding data generation capability.

• Many models of GAN have proposed, and several practical applications emerged in various domains of computer vision and machine learning.
• Despite GAN's excellent success, there are still obstacles to stable training.
• The problems are due to Nash-equilibrium, internal covariate shift, mode collapse, vanishing gradient, and lack of proper evaluation metrics.