Generative adversarial network

GANs are generative adversarial networks. They were developed by Ian Goodfellow.
Goal: Learn to generate examples from the same distribution as your training set.

Basis Structure

GANs consist of a generator and a discriminator.

For iteration i
  For iteration j
    Update Discriminator
  Update Generator

Variations

Wasserstein GAN

Paper
Medium post
This new WGAN-GP loss function improves the stability of training.

Applications

SinGAN

Main article: SinGAN

Paper
Website
Github Official PyTorch Implementation
SinGAN: Learning a Generative Model from a Single Natural Image