Image style transfer with collection representation space and semantic-guided reconstruction.

Image style transfer renders the content of an image into different styles. Current methods made decent progress with transferring the style of single image, however, visual statistics from one image cannot reflect the full scope of an artist. Also, previous work did not put content preservation in the important position, which would result in poor structure integrity, thus deteriorating the comprehensibility of generated image. These two problems would limit the visual quality improvement of style transfer results. Targeting at style resemblance and content preservation problems, we propose a style transfer system composed of collection representation space and semantic-guided reconstruction. We train an encoder–decoder network with art collections to construct a representation space that can reflect the style of the artist. Then, we use semantic information as guidance to reconstruct the target representation of the input image for better content preservation. We conduct both quantitative analysis and qualitative evaluation to assess the proposed method. Experiment results demonstrate that our approach well balanced the trade-off between capturing artistic characteristics and preserving content information in style transfer tasks.