Generative adversarial networks

Generative adversarial networks are relatively new branch of unsupervised machine learning, implemented by a system of two neural networks. It was introduced by Ian Goodfellow et al in 2014.

Overview

A generative model starts with random pixels as the initial input and generates output data that is then compared to training data sets by the other, adversarial process, usually implemented by a standard convolutional neural network.^[1] The generator is trying to adjust parameters so that the training data and generated data can not be distinguished from each other anymore by the discriminator model. The goal is to find a setting of parameters that makes generated data look like the training data to the discriminator network.^[2]

Improving sample diversity

Too narrow distribution of generated data has been solved by Tim Salimans et al at OpenAI in 2016.^[3][4]

Application

GANs can be used to produce high quality samples of photorealistic images for the purposes of visualizing new interior/industrial design, shoes, bags, and clothing items or items for computer games scenes. These networks have been reported to be used heavily by Facebook, too.^[5]

References