An Introduction to Generative Adversarial Networks

Generative Adversarial Network(GAN) is a different kind of deep learning method that is used for generating new data that looks like data from the dataset it was trained on. A GAN is a combination of two distinct neural networks in a contest with each other. One of those is the generator, trained to generate new examples and the other is the discriminator, which is trained to classify whether examples are real or generated(fake). GANs were first introduced in the paper "Generative Adversarial Nets" written by Ian J. Goodfellow et al. back in 2014. Since then, GANs have come a long way.