  In recent years, there have been tremendous advancements in the field of
machine learning. These advancements have been made through both academic as
well as industrial research. Lately, a fair amount of research has been
dedicated to the usage of generative models in the field of computer vision and
image classification. These generative models have been popularized through a
new framework called Generative Adversarial Networks. Moreover, many modified
versions of this framework have been proposed in the last two years. We study
the original model proposed by Goodfellow et al. as well as modifications over
the original model and provide a comparative analysis of these models.
