Generative adversarial networks are one of
the recent research areas in deep learning. It is used in
various applications in image/text/video generations etc.
GANs are widely known for the adversarial process it
follows and the two models in its architecture – the
generator and the discriminator. Since it gives better
results than other generative models it is preferred
more. In this paper initially we discuss the basic
architecture of GANs, the mathematical concept
involved in it and how it outshines other generative
models. The various applications of GANs to name a
few, implementation of GANs in Radiology, beauty
GANs, GANs deployed in cyber security and attention
prediction have been investigated and mentioned with
a brief description about the same. Further in the end
we have discussed the challenges faced by it and its
future scope in the fourth coming years.
Keywords : Generative Adversarial Network, Generator, Discriminator, Adversarial Process, Deep Learning.