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Gorpincenko, Artjoms, French, Geoffrey and Mackiewicz, Michal 
ORCID: https://orcid.org/0000-0002-8777-8880
(2021)
Virtual adversarial training in feature space to improve unsupervised video domain adaptation.
Electronic Imaging, 2021 (10).
258-1-258-6.
ISSN 2470-1173
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Abstract
Virtual Adversarial Training has recently seen a lot of success in semi-supervised learning, as well as unsupervised Domain Adaptation. However, so far it has been used on input samples in the pixel space, whereas we propose to apply it directly to feature vectors. We also discuss the unstable behaviour of entropy minimization and Decision-Boundary Iterative Refinement Training With a Teacher in Domain Adaptation, and suggest substitutes that achieve similar behaviour. By adding the aforementioned techniques to the state of the art model TA3N, we either maintain competitive results or outperform prior art in multiple unsupervised video Domain Adaptation tasks.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | computer graphics and computer-aided design,computer science applications,human-computer interaction,software,electrical and electronic engineering,atomic and molecular physics, and optics ,/dk/atira/pure/subjectarea/asjc/1700/1704 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Collaborative Centre for Sustainable Use of the Seas Faculty of Science > Research Groups > Colour and Imaging Lab |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 24 Sep 2021 01:05 |
| Last Modified: | 17 Sep 2023 06:30 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/81506 |
| DOI: | 10.2352/ISSN.2470-1173.2021.10.IPAS-258 |
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