Real-time Universal Style Transfer on High-resolution Images via Zero-channel Pruning

• URL: http://arxiv.org/abs/2006.09029v2
• Date: Tue, 23 Jun 2020 03:37:40 GMT
• Title: Real-time Universal Style Transfer on High-resolution Images via Zero-channel Pruning
• Authors: Jie An, Tao Li, Haozhi Huang, Li Shen, Xuan Wang, Yongyi Tang, Jinwen Ma, Wei Liu, and Jiebo Luo
• Abstract summary: ArtNet can achieve universal, real-time, and high-quality style transfer on high-resolution images simultaneously. By using ArtNet and S2, our method is 2.3 to 107.4 times faster than state-of-the-art approaches.
• Score: 74.09149955786367
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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).

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