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).

本文提出了一种基于 ArtNet 的，在风格转移应用特别设计的零通道修剪算法以及一种理论上有根据的三明治交换变换（S2）模块，可以实现高分辨率图像上的通用，实时和高质量的风格转移。经全面实验验证，ArtNet 能够同时实现 68.03 FPS 的通用，实时，高质量的 512 x 512 图像的风格转移，较先前最先进的方法速度提高 2.3 到 107.4 倍。