Neural Style Transfer: A Review

The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style. This process of using CNNs to render a content image in different styles is referred to as Neural Style Transfer (NST). Since then, NST has become a trending topic both in academic literature and industrial applications. It is receiving increasing attention and a variety of approaches are proposed to either improve or extend the original NST algorithm. In this paper, we aim to provide a comprehensive overview of the current progress towards NST. We first propose a taxonomy of current algorithms in the field of NST. Then, we present…

• NN
  National Natural Science Foundation of China

  Awards: 61572428, U1509206
• NK
  National Key Research and Development Program of China

  Award: 2016YFB1200203
• FR
  Fundamental Research Funds for the Central Universities
• IS
  International Science and Technology Cooperation Programme

  Award: 2013DFG12840