Image Translation by Domain-Adversarial Training
Joint Authors
Zhao, Yanwei
Wang, Wan Liang
Li, Zhuorong
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-06-26
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Image translation, where the input image is mapped to its synthetic counterpart, is attractive in terms of wide applications in fields of computer graphics and computer vision.
Despite significant progress on this problem, largely due to a surge of interest in conditional generative adversarial networks (cGANs), most of the cGAN-based approaches require supervised data, which are rarely available and expensive to provide.
Instead we elaborate a common framework that is also applicable to the unsupervised cases, learning the image prior by conditioning the discriminator on unaligned targets to reduce the mapping space and improve the generation quality.
Besides, domain-adversarial training inspired by domain adaptation is proposed to capture discriminative and expressive features, for the purpose of improving fidelity.
Effectiveness of our method is demonstrated by compelling experimental results of our method and comparisons with several baselines.
As for the generality, it could be analyzed from two perspectives: adaptation to both supervised and unsupervised setting and the diversity of tasks.
American Psychological Association (APA)
Li, Zhuorong& Wang, Wan Liang& Zhao, Yanwei. 2018. Image Translation by Domain-Adversarial Training. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1130847
Modern Language Association (MLA)
Li, Zhuorong…[et al.]. Image Translation by Domain-Adversarial Training. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1130847
American Medical Association (AMA)
Li, Zhuorong& Wang, Wan Liang& Zhao, Yanwei. Image Translation by Domain-Adversarial Training. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1130847
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130847