  Face sketch synthesis has made great progress in the past few years. Recent
methods based on deep neural networks are able to generate high quality
sketches from face photos. However, due to the lack of training data
(photo-sketch pairs), none of such deep learning based methods can be applied
successfully to face photos in the wild. In this paper, we propose a
semi-supervised deep learning architecture which extends face sketch synthesis
to handle face photos in the wild by exploiting additional face photos in
training. Instead of supervising the network with ground truth sketches, we
first perform patch matching in feature space between the input photo and
photos in a small reference set of photo-sketch pairs. We then compose a pseudo
sketch feature representation using the corresponding sketch feature patches to
supervise our network. With the proposed approach, we can train our networks
using a small reference set of photo-sketch pairs together with a large face
photo dataset without ground truth sketches. Experiments show that our method
achieve state-of-the-art performance both on public benchmarks and face photos
in the wild. Codes are available at
https://github.com/chaofengc/Face-Sketch-Wild.
