P$^2$-GAN: Efficient Style Transfer Using Single Style Image

• URL: http://arxiv.org/abs/2001.07466v2
• Date: Thu, 30 Jan 2020 16:37:22 GMT
• Title: P$^2$-GAN: Efficient Style Transfer Using Single Style Image
• Authors: Zhentan Zheng, Jianyi Liu
• Abstract summary: Style transfer is a useful image synthesis technique that can re-render given image into another artistic style. Generative Adversarial Network (GAN) is a widely adopted framework toward this task for its better representation ability on local style patterns. In this paper, a novel Patch Permutation GAN (P$2$-GAN) network that can efficiently learn the stroke style from a single style image is proposed.
• Score: 2.703193151632043
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 (P$^2$-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.

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