论文标题
p $^2 $ - gan：使用单样式图像的高效样式转移
P$^2$-GAN: Efficient Style Transfer Using Single Style Image
论文作者
论文摘要
样式转移是一种有用的图像合成技术，可以在保留其内容信息的同时将图像重新呈现为另一种艺术风格。生成对抗网络（GAN）是针对此任务的广泛采用的框架，其在本地样式模式上的表现能力比基于革兰氏集的传统方法更好。但是，大多数以前的方法都依靠足够数量的预收集样式图像来训练模型。在本文中，提出了一个新颖的补丁置换gan（p $^2 $ gan）网络，该网络可以从单个样式图像中有效地学习中风样式。我们使用补丁置换量来生成来自给定样式图像的多个培训样本。可以设计一个可以同时处理贴片图像和自然图像的贴片判别器。我们还提出了一个基于本地纹理描述的标准，以定量评估样式转移质量。实验结果表明，与许多最先进的方法相比，我们的方法可以从单样式图像产生更优质的重新效果，并提高了计算效率。
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.
