  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://acht7111020.github.io/DSMAP-demo/.
