  Generative adversarial networks (GANs) are a method based on the training of
two neural networks, one called generator and the other discriminator,
competing with each other to generate new instances that resemble those of the
probability distribution of the training data. GANs have a wide range of
applications in fields such as computer vision, semantic segmentation, time
series synthesis, image editing, natural language processing, and image
generation from text, among others. Generative models model the probability
distribution of a data set, but instead of providing a probability value, they
generate new instances that are close to the original distribution. GANs use a
learning scheme that allows the defining attributes of the probability
distribution to be encoded in a neural network, allowing instances to be
generated that resemble the original probability distribution. This article
presents the theoretical foundations of this type of network as well as the
basic architecture schemes and some of its applications. This article is in
Spanish to facilitate the arrival of this scientific knowledge to the
Spanish-speaking community.
