Generative Adversarial Networks: An Overview

2017/10/19 by Antonia Creswell, Tom White, Vincent Dumoulin +4 · 1 voice · 103 citations
Computer Science · #Generative Adversarial Networks and Image Synthesis #Advanced Image Processing Techniques #Image and Signal Denoising Methods

paper · pdf · doi:10.1109/msp.2017.2765202

Abstract

Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this by 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 superresolution, and classification. The aim of this review article is to provide an overview of GANs for the signal processing community, drawing on familiar analogies and concepts where possible. In addition to identifying different methods for training and constructing GANs, we also point to remaining challenges in their theory and application.

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