In essence, Generative Adversarial Networks consists of two neural networks: a discriminator and a generator. In essence, they are playing an ongoing game in which the discriminator determines the legitimacy of the data produced by the generator. The generator's goal is to produce data that is so realistic that the generated data can fool it, and the discriminator's job is to distinguish between the generated and genuine data. These dynamic adversarial processes allow GANs to produce remarkably realistic outputs. To Know More: https://digicrusader.com/g...