Abstract:
Generative Adversarial Networks are artificial neural networks that pit two different sets of neural networks
against one another in order to generate data that isn't part of the training set. The Generative Adversarial Network
(GAN) produces good outcomes when it is trained on image data that comes from the actual world. The generator and the
discriminator make up the Generative Adversarial Network (GAN), which stands for "generative adversarial network."
The parameters that were utilized to generate the data are completely arbitrary. The information is evaluated, and
erroneous information is distinguished from true information by the discriminator. Several researchers has investigated
various types of GANs but comprehensive analysis and comparison of different types of recent GAN’s has not been done in
literature. The article concludes with a discussion of the possible uses of GANs in a variety of settings, as well as how
these applications constitute a fascinating new area of research and prospective expansion.
