Semi-supervised Cycle-GAN for face photo-sketch translation in the wild

TL;DR

This paper introduces Semi-Cycle-GAN, a semi-supervised method with noise injection for face photo-sketch translation that effectively leverages small paired datasets and large unpaired datasets, improving results in wild conditions.

Contribution

The paper proposes a novel semi-supervised Cycle-GAN approach with pseudo sketch features and noise injection to address data scarcity and the steganography issue in face photo-sketch translation.

Findings

Achieves competitive performance on public benchmarks.

Produces superior results on photos in the wild.

Effectively alleviates steganography phenomenon.

Abstract

The performance of face photo-sketch translation has improved a lot thanks to deep neural networks. GAN based methods trained on paired images can produce high-quality results under laboratory settings. Such paired datasets are, however, often very small and lack diversity. Meanwhile, Cycle-GANs trained with unpaired photo-sketch datasets suffer from the \emph{steganography} phenomenon, which makes them not effective to face photos in the wild. In this paper, we introduce a semi-supervised approach with a noise-injection strategy, named Semi-Cycle-GAN (SCG), to tackle these problems. For the first problem, we propose a {\em pseudo sketch feature} representation for each input photo composed from a small reference set of photo-sketch pairs, and use the resulting {\em pseudo pairs} to supervise a photo-to-sketch generator $G_{p2s}$. The outputs of $G_{p2s}$ can in turn help to train a sketch-to-photo generator $G_{s2p}$ in a self-supervised manner. This allows us to train $G_{p2s}$ and $G_{s2p}$ using a small reference set of photo-sketch pairs together with a large face photo dataset (without ground-truth sketches). For the second problem, we show that the simple noise-injection strategy works well to alleviate the \emph{steganography} effect in SCG and helps to produce more reasonable sketch-to-photo results with less overfitting than fully supervised approaches. Experiments show that SCG achieves competitive performance on public benchmarks and superior results on photos in the wild.