Generative Adversarial Networks (GANs): Engine and Applications

GANs were introduced by Ian Goodfellow in 2014. Both of them are dedicated to extract features from data by learning the identity function f(x) x and both of them rely on Markov chains to train or to generate samples. Generative adversarial networks were designed to avoid using Markov chains because of the high computational cost of the latter. Another advantage relative to Boltzmann machines is that the Generator function has much fewer restrictions (there are only a few probability distributions that admit Markov chain sampling). In this article, we'll tell you how generative adversarial nets work and what their most popular applications in real life are.