# P2-GAN: Efficient Style Transfer Using Single Style Image

arXiv: https://arxiv.org/abs/2001.07466 · HTML: https://mlsanity.com/paper/2001.07466/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2020.

## Built on by (most cited first, 3 of 3)

- [SSTN: Self-Supervised Domain Adaptation Thermal Object Detection for Autonomous Driving](https://mlsanity.com/paper/2103.03150/) (2021)
- [Terrain Diffusion Network: Climatic-Aware Terrain Generation with Geological Sketch Guidance](https://mlsanity.com/paper/2308.16725/) (2023)
- [ShufaNet: Classification method for calligraphers who have reached the professional level](https://mlsanity.com/paper/2111.11350/) (2021)

## Abstract (arXiv)

Style transfer is a useful image synthesis technique that can re-render given image into another artistic style while preserving its content information. Generative Adversarial Network (GAN) is a widely adopted framework toward this task for its better representation ability on local style patterns than the traditional Gram-matrix based methods. However, most previous methods rely on sufficient amount of pre-collected style images to train the model. In this paper, a novel Patch Permutation GAN (P2-GAN) network that can efficiently learn the stroke style from a single style image is proposed. We use patch permutation to generate multiple training samples from the given style image. A patch discriminator that can simultaneously process patch-wise images and natural images seamlessly is designed. We also propose a local texture descriptor based criterion to quantitatively evaluate the style transfer quality. Experimental results showed that our method can produce finer quality re-renderings from single style image with improved computational efficiency compared with many state-of-the-arts methods.