dc.citation.conferencePlace |
US |
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dc.citation.conferencePlace |
Seattle, WA, USA |
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dc.citation.endPage |
8194 |
- |
dc.citation.startPage |
8185 |
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dc.citation.title |
IEEE Conference on Computer Vision and Pattern Recognition |
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dc.contributor.author |
Choi, Yunjey |
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dc.contributor.author |
Uh, Youngjung |
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dc.contributor.author |
Yoo, Jaejun |
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dc.contributor.author |
Ha, Jung-Woo |
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dc.date.accessioned |
2024-01-31T22:41:24Z |
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dc.date.available |
2024-01-31T22:41:24Z |
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dc.date.created |
2021-08-19 |
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dc.date.issued |
2020-08 |
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dc.description.abstract |
A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains. Existing methods address either of the issues, having limited diversity or multiple models for all domains. We propose StarGAN v2, a single framework that tackles both and shows significantly improved results over the baselines. Experiments on CelebA-HQ and a new animal faces dataset (AFHQ) validate our superiority in terms of visual quality, diversity, and scalability. To better assess image-to-image translation models, we release AFHQ, high-quality animal faces with large inter-and intra-domain differences. The code, pretrained models, and dataset are available at https://github.com/clovaai/stargan-v2. |
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dc.identifier.bibliographicCitation |
IEEE Conference on Computer Vision and Pattern Recognition, pp.8185 - 8194 |
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dc.identifier.doi |
10.1109/CVPR42600.2020.00821 |
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dc.identifier.issn |
1063-6919 |
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dc.identifier.scopusid |
2-s2.0-85094098501 |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/78348 |
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dc.identifier.url |
https://ieeexplore.ieee.org/document/9157662 |
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dc.language |
영어 |
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dc.publisher |
IEEE Computer Society |
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dc.title |
StarGAN v2: Diverse Image Synthesis for Multiple Domains |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2020-06-14 |
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