GANs for Image Generation

Xudong Mao () and Qing Li ()
Additional contact information
Xudong Mao: Hong Kong Polytechnic University, Department of Computing
Qing Li: Hong Kong Polytechnic University, Department of Computing

Chapter Chapter 2 in Generative Adversarial Networks for Image Generation, 2021, pp 9-52 from Springer

Abstract: Abstract Deep learning has proven to be hugely successful in computer vision and has even been applied to many real-world tasks, such as image classification (He et al. 2016), object detection (Ren et al. 2015), and segmentation (Long et al. 2015). Compared with these tasks in supervised learning, however, image generation, which belongs to unsupervised learning, may not achieve the desired performance. The target of image generation is to learn to draw pictures by means of some generative models, as shown in Fig. 2.1.

Date: 2021
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:978-981-33-6048-8_2

Ordering information: This item can be ordered from
http://www.springer.com/9789813360488

DOI: 10.1007/978-981-33-6048-8_2

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 ().