5^th World Machine Learning and Deep Learning Congress

Biography

Biography: Mr. Miguel Ángel Martínez

Abstract

Deep Convolutional Generative Adversarial Networks are a class of unsupervised machine learning algorithm, implemented by a system of two neural networks contesting with each other in a zero-sum game framework.

First introduced by Ian Goodfellow et al. in 2014, GANs are a new framework for estimating models via an adversarial process, in which two models are trained simultaneously: 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. 

The most important benefit of GANs is their ability to learn deep representations without extensively annotated training data. Since their introduction, they have been proven to be useful in a variety of applications, including image synthesis, semantic image editing, style transfer, image super-resolution and classification.