Generative Adversarial Networks

Gilad Cohen () and Raja Giryes ()
Additional contact information
Gilad Cohen: Tel Aviv University
Raja Giryes: Tel Aviv University

A chapter in Machine Learning for Data Science Handbook, 2023, pp 375-400 from Springer

Abstract: Abstract Generative adversarial networks (GANs) are very popular frameworks for generating high-quality data and are immensely used in both the academia and industry in many domains. Arguably, their most substantial impact has been in the area of computer vision, where they achieve state-of-the-art image generation. This chapter gives an introduction to GANs, by discussing their principle mechanism and presenting some of their inherent problems during training and evaluation. We focus on these three issues: (1) mode collapse, (2) vanishing gradients, and (3) generation of low-quality images. We then list some architecture-variant and loss-variant GANs that remedy the above challenges. Lastly, we present two utilization examples of GANs for real-world applications: data augmentation and face images generation.

Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-24628-9_17

Ordering information: This item can be ordered from
http://www.springer.com/9783031246289

DOI: 10.1007/978-3-031-24628-9_17

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().