Chang H.-YWang ZYUNG-YU CHUANG2021-09-022021-09-02202003029743https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097406146&doi=10.1007%2f978-3-030-58598-3_34&partnerID=40&md5=502149bd7e3c0be621797a5a8f00bf35https://scholars.lib.ntu.edu.tw/handle/123456789/581486Style 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.Mapping; Semantics; Content representation; Domain specific; Image translation; Remapping; Semantic correspondence; Shared contents; Transfer method; Computer visionconference paper10.1007/978-3-030-58598-3_342-s2.0-85097406146