Generative Adversarial Networks: An Overview

Understand

Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data.

• They achieve this through deriving backpropagation signals through a competitive process involving a pair of networks.
• The representations that can be learned by GANs may be used in a variety of applications, including image synthesis, semantic image editing, style transfer, image super-resolution and classification.
• The aim of this review paper is to provide an overview of GANs for the signal processing community, drawing on familiar analogies and concepts where possible.