Respecting low-level components of content with skip connections and semantic information in image style transfer

Style transfer represents the most creative task of deep learning, creating a virtual world in art. However, most of the current deep networks concentrate on style and ignore to exploit low-level components of content such as edges, shapes, which define objects in a virtualized image. In our study, we present a scheme to use skip connections and leverage semantic information as input, which effectively preserve low-level components in great detail. To understand the meaning of our added components, besides the ablation study to visualize the proficiency, we propose to use constrained hyper-parameters for all skip connections to find how each layer influence on stylized images. Our models are trained on images in COCO-stuff with their semantic maps and testing without them. We also compare our work to previous works. As a result, our method outperforms in retaining definable details of content with significant style using skip connections, especially semantic information.