A brief study of generative adversarial networks and their applications in image synthesis

Image Synthesis (IS), an expansion to Artificial Intelligence (AI) and Computer Vision, is the technique of artificially producing images that retains some specific required contents. An adequate procedure to handle IS problem is to tackle it using the Deep Generative Models. Generative Models are broadly utilized in numerous sub fields of AI and have empowered versatile demonstration of perplexing scenarios including image, text and music. In this paper, a particular class of Deep Generative model namely Generative Adversarial Networks (GAN) has been considered to provide a way to acquire deep illustrations derived from backpropagation signals and without the use of wide range of annotated training data. The design of GAN architecture plays a key role in image synthesis and the motive behind this paper is to analyse GAN architecture based on different variants of GANs with respect to Image Synthesis. Furthermore, a compact categorization of GANs along with their key features, pros and cons have been investigated to identify the research challenges in this field.