# Semi-Supervised Learning for Face Sketch Synthesis in the Wild

## TL;DR

This paper introduces a semi-supervised deep learning method for face sketch synthesis that effectively utilizes limited paired data and large unpaired face photo datasets to generate high-quality sketches in wild conditions.

## Contribution

It presents a novel semi-supervised architecture that uses patch matching in feature space and pseudo sketch supervision, enabling face sketch synthesis with minimal paired data.

## Key findings

- Achieves state-of-the-art results on public benchmarks.
- Effectively handles face photos in the wild.
- Utilizes small reference sets with large unpaired datasets.

## Abstract

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.