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|a 10.1109/TPAMI.2023.3284003
|2 doi
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|a eng
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| 100 |
1 |
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|a Yang, Shuai
|e verfasserin
|4 aut
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| 245 |
1 |
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|a GP-UNIT
|b Generative Prior for Versatile Unsupervised Image-to-Image Translation
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|c 2023
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 06.09.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Recent advances in deep learning have witnessed many successful unsupervised image-to-image translation models that learn correspondences between two visual domains without paired data. However, it is still a great challenge to build robust mappings between various domains especially for those with drastic visual discrepancies. In this paper, we introduce a novel versatile framework, Generative Prior-guided UNsupervised Image-to-image Translation (GP-UNIT), that improves the quality, applicability and controllability of the existing translation models. The key idea of GP-UNIT is to distill the generative prior from pre-trained class-conditional GANs to build coarse-level cross-domain correspondences, and to apply the learned prior to adversarial translations to excavate fine-level correspondences. With the learned multi-level content correspondences, GP-UNIT is able to perform valid translations between both close domains and distant domains. For close domains, GP-UNIT can be conditioned on a parameter to determine the intensity of the content correspondences during translation, allowing users to balance between content and style consistency. For distant domains, semi-supervised learning is explored to guide GP-UNIT to discover accurate semantic correspondences that are hard to learn solely from the appearance. We validate the superiority of GP-UNIT over state-of-the-art translation models in robust, high-quality and diversified translations between various domains through extensive experiments
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Jiang, Liming
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Ziwei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Loy, Chen Change
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 10 vom: 08. Okt., Seite 11869-11883
|w (DE-627)NLM098212257
|x 1939-3539
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|g volume:45
|g year:2023
|g number:10
|g day:08
|g month:10
|g pages:11869-11883
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|u http://dx.doi.org/10.1109/TPAMI.2023.3284003
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