Proceedings of the 27th International Conference on Neural Information Processing Systems 2014

Generative Adversarial Networks

Proceedings of the 27th International Conference on Neural Information Processing Systems 2014 eriklindernoren/Keras-GAN

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake.