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

arXiv: https://arxiv.org/abs/1812.04929 · HTML: https://mlsanity.com/paper/1812.04929/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2018.

## Built on by (most cited first, 7 of 7)

- [15M Multimodal Facial Image-Text Dataset](https://mlsanity.com/paper/2407.08515/) (2024)
- [Unconstrained Face Sketch Synthesis via Perception-Adaptive Network and A New Benchmark](https://mlsanity.com/paper/2112.01019/) (2021)
- [Human-Inspired Facial Sketch Synthesis with Dynamic Adaptation](https://mlsanity.com/paper/2309.00216/) (2023)
- [Semi-supervised Cycle-GAN for face photo-sketch translation in the wild](https://mlsanity.com/paper/2307.10281/) (2023)
- [Bridging Unpaired Facial Photos And Sketches By Line-drawings](https://mlsanity.com/paper/2102.00635/) (2021)
- [Multiple Noises in Diffusion Model for Semi-Supervised Multi-Domain Translation](https://mlsanity.com/paper/2309.14394/) (2023)
- [Multi-Style Facial Sketch Synthesis through Masked Generative Modeling](https://mlsanity.com/paper/2408.12400/) (2024)

## Abstract (arXiv)

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.