The counterfeiter learns to make simulated bills, and the policeman learns to detect them. Just imagine GAN as a counterfeiter and a policeman competing with each other. The generative neural network creates samples, and the discriminative tries to distinguish correct samples from incorrect ones.
The generative-adversarial network consists of two parts: generative and discriminative. In this article, we are going to take a deep dive into what the generative models are, the recent developments in the field, and the usage of GANs in business.
It is proved by existing GAN applications. Can a machine attempt to approach the task of creating unique content that would be indistinguishable from human-produced artefacts? Is it possible to do this with the help of generative adversarial networks (GANs) - by learning the structure of the complex real-world data examples and generating similar synthetic examples that are bound by the same structure? With the recent advances in the development of generative models, the answer seems to be yes, at least to an extent.
