  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. Therefore, stable training is
a crucial issue in different applications for the success of GAN. Herein, we
survey several training solutions proposed by different researchers to
stabilize GAN training. We survey, (I) the original GAN model and its modified
classical versions, (II) detail analysis of various GAN applications in
different domains, (III) detail study about the various GAN training obstacles
as well as training solutions. Finally, we discuss several new issues as well
as research outlines to the topic.
