论文标题
通过零通道修剪的高分辨率图像上的实时通用风格转移
Real-time Universal Style Transfer on High-resolution Images via Zero-channel Pruning
论文作者
论文摘要
提取有效的深层功能来表示内容和样式信息是通用样式转移的关键。大多数现有的算法使用VGG19作为特征提取器，它会产生高计算成本，并阻碍了高分辨率图像上的实时样式转移。在这项工作中，我们提出了一种轻巧的替代体系结构-Artnet，该体系结构基于Googlenet，后来由一种新颖的频道修剪方法修剪，名为零通道修剪，专门设计用于样式转移方法。此外，我们提出了一个理论上声音的三明治交换变换（S2）模块以传递深层功能，这可以创造出令人愉悦的整体外观和良好的本地纹理，并具有提高的内容保存能力。通过使用Artnet和S2，我们的方法比最先进的方法快2.3至107.4倍。全面的实验表明，Artnet可以同时在高分辨率图像上实现通用，实时和高质量的风格转移（512倍512张图像的68.03 fps）。
Extracting effective deep features to represent content and style information is the key to universal style transfer. Most existing algorithms use VGG19 as the feature extractor, which incurs a high computational cost and impedes real-time style transfer on high-resolution images. In this work, we propose a lightweight alternative architecture - ArtNet, which is based on GoogLeNet, and later pruned by a novel channel pruning method named Zero-channel Pruning specially designed for style transfer approaches. Besides, we propose a theoretically sound sandwich swap transform (S2) module to transfer deep features, which can create a pleasing holistic appearance and good local textures with an improved content preservation ability. By using ArtNet and S2, our method is 2.3 to 107.4 times faster than state-of-the-art approaches. The comprehensive experiments demonstrate that ArtNet can achieve universal, real-time, and high-quality style transfer on high-resolution images simultaneously, (68.03 FPS on 512 times 512 images).
