Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (19): 32-43.DOI: 10.3778/j.issn.1002-8331.2105-0296

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Review of Neural Style Transfer Models

TANG Renwei, LIU Qihe, TAN Hao   

  1. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
  • Online:2021-10-01 Published:2021-09-29



  1. 电子科技大学 信息与软件学院,成都 610054


Neural Style Transfer(NST) technique is used to simulate different art styles of images and videos, which is a popular topic in computer vision. This paper aims to provide a comprehensive overview of the current progress towards NST. Firstly, the paper reviews the Non-Photorealistic Rendering(NPR) technique and traditional texture transfer. Then, the paper categorizes current major NST methods and gives a detailed description of these methods along with their subsequent improvements. After that, it discusses various applications of NST and presents several evaluation methods which compares different style transfer models both qualitatively and quantitatively. In the end, it summarizes the existing problems and provides some future research directions for NST.

Key words: Neural Style Transfer(NST), deep learning, convolution neural network, generative model, generative adversarial network



关键词: 神经风格迁移, 深度学习, 卷积神经网络, 生成模型, 生成对抗网络