  Generative Adversarial Networks have been crucial in the developments made in
unsupervised learning in recent times. Exemplars of image synthesis from text
or other images, these networks have shown remarkable improvements over
conventional methods in terms of performance. Trained on the adversarial
training philosophy, these networks aim to estimate the potential distribution
from the real data and then use this as input to generate the synthetic data.
Based on this fundamental principle, several frameworks can be generated that
are paragon implementations in several real-life applications such as art
synthesis, generation of high resolution outputs and synthesis of images from
human drawn sketches, to name a few. While theoretically GANs present better
results and prove to be an improvement over conventional methods in many
factors, the implementation of these frameworks for dedicated applications
remains a challenge. This study explores and presents a taxonomy of these
frameworks and their use in various image to image synthesis and text to image
synthesis applications. The basic GANs, as well as a variety of different niche
frameworks, are critically analyzed. The advantages of GANs for image
generation over conventional methods as well their disadvantages amongst other
frameworks are presented. The future applications of GANs in industries such as
healthcare, art and entertainment are also discussed.
