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Unpaired faces to cartoons: improving XGAN

Abstract:

Domain Adaptation is a task that aims to translate an image from a source domain to a desired target domain. Current methods in domain adaptation use adversarial training based on Generative Adversarial Networks (GAN). In the present work, we focus on the task of domain adaptation from real faces to cartoon face images. We start from a baseline architecture called XGAN and introduce some improvements to it. Our proposed model is called W-XDGAN, which uses a form of GAN called Wasserstein-GAN,...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/cvprw56347.2022.00158

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Institution:
University of Oxford
Division:
MSD
Department:
RDM
Role:
Author
Publisher:
IEEE
Host title:
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Pages:
1517-1526
Publication date:
2022-08-23
Acceptance date:
2022-03-02
Event title:
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Event location:
New Orleans, Louisiana, USA
Event website:
https://cvpr2022.thecvf.com/
Event start date:
2022-06-19
Event end date:
2022-06-20
DOI:
EISSN:
2160-7516
ISSN:
2160-7508
Language:
English
Keywords:
Pubs id:
1335545
Local pid:
pubs:1335545
Deposit date:
2023-04-04

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