A GUIDE TO GANS (GENERATIVE ADVERSARIAL NETWORKS)

Generative adversarial networks (GANS) are machine learning approaches that utilize two convolutional neural networks. These networks contest to generate new data instances similar to the training set. Deep learning experts, Ian Good-fellow, together with his colleagues, were the first to propose GANS in the 2014 NeurlPS paper.

GAN is structured into two: The generator that learns to produce plausible data and discriminator that distinguishes fake data from real data produced by the generator. The discriminator also penalizes the generator for generating implausible results.

Use cases of GANs

Generative Adversarial Networks (GANS) has both positive and negative uses as they can learn to imitate