Generative adversarial networks - an introduction

Hamed Alqahtani*, Manolya Kavakli-Thorne, Gulshan Kumar

*Corresponding author for this work

Research output: Chapter in Book/Published conference outputChapter


Generative adversarial networks (GANs) present a way to learn deep representations without extensively annotated training data. These networks achieve learning through deriving back propagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in several applications. GANs have made significant advancements and tremendous performance in numerous applications. The essential applications include semantic image editing, style transfer, image synthesis, image super-resolution and classification. This chapter aims to present an overview of GANs, and its different variants. The chapter attempts to identify GANs' advantages, disadvantages and significant challenges to the successful implementation of GAN in different application areas. Finally, the chapter ends with the conclusion and future aspects.

Original languageEnglish
Title of host publicationImage Recognition
Subtitle of host publication Progress, Trends and Challenges
PublisherNova Science Publishers Inc
Number of pages28
ISBN (Electronic)9781536172591
ISBN (Print)9781536172584
Publication statusPublished - 12 May 2020

Bibliographical note

Publisher Copyright:
© 2020 by Nova Science Publishers, Inc. All rights reserved.


  • Generative adversarial networks
  • Neural networks
  • Supervised learning
  • Unsupervised learning


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