CLAST : Contrastive Learning for Arbitrary Style Transfer
Arbitrary style transfer aims at migrating the style of a reference style painting to a target content image. Existing methods find it challenging to achieve good content fidelity and style migration at the same time. Moreover, they all rely on manually defined content and style, which is of limited...
| Publié dans: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 31(2022) vom: 25., Seite 6761-6772 |
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| Auteur principal: | |
| Autres auteurs: | , , |
| Format: | Article en ligne |
| Langue: | English |
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2022
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| Accès à la collection: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Sujets: | Journal Article |
| Accès en ligne |
Volltext |