Generative adversarial network

Generative Adversarial Network (GAN) is a deep learning method that simultaneously trains two networks. A generator that learns to generate fake samples from an unknown distribution or noise and a discriminator that learns to identify fake from real samples.

It aims to model the natural image distribution by forcing the generated samples to be indistinguishable from natural images. GANs enable a various applications such as image generation, representation learning, image manipulation, object detection and video applications.