https://scholars.lib.ntu.edu.tw/handle/123456789/581486
標題: | Domain-Specific Mappings for Generative Adversarial Style Transfer | 作者: | Chang H.-Y Wang Z Chuang Y.-Y. YUNG-YU CHUANG |
關鍵字: | Mapping; Semantics; Content representation; Domain specific; Image translation; Remapping; Semantic correspondence; Shared contents; Transfer method; Computer vision | 公開日期: | 2020 | 卷: | 12353 LNCS | 起(迄)頁: | 573-589 | 來源出版物: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 摘要: | Style transfer generates an image whose content comes from one image and style from the other. Image-to-image translation approaches with disentangled representations have been shown effective for style transfer between two image categories. However, previous methods often assume a shared domain-invariant content space, which could compromise the content representation power. For addressing this issue, this paper leverages domain-specific mappings for remapping latent features in the shared content space to domain-specific content spaces. This way, images can be encoded more properly for style transfer. Experiments show that the proposed method outperforms previous style transfer methods, particularly on challenging scenarios that would require semantic correspondences between images. Code and results are available at https://github.com/acht7111020/DSMAP. ? 2020, Springer Nature Switzerland AG. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097406146&doi=10.1007%2f978-3-030-58598-3_34&partnerID=40&md5=502149bd7e3c0be621797a5a8f00bf35 https://scholars.lib.ntu.edu.tw/handle/123456789/581486 |
ISSN: | 03029743 | DOI: | 10.1007/978-3-030-58598-3_34 |
顯示於: | 資訊工程學系 |
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