Advanced deep learning techniques for image style transfer: A survey

Abstract

Image style transfer is an emerging technique based on deep learning, which takes advantage of the impressive feature extraction of convolutional neural networks (CNN). The extraction of high-level features of images makes the separation of style information and image content possible. Image style conversion technique aims to learn the style characteristics of various paintings, and then apply the learned style to another image. The combination of artificial intelligence and art makes this technique highly concerned in the relevant technical fields and art fields, and has been applied in many different fields of society. In this paper, we conduct a comprehensive study on image style transfer techniques. Firstly, we analyze and classify the existing algorithms of the current style transfer algorithms, and then elaborate on their applications in different fields. In addition, we also summarize the future development and prospect of the image transfer technique.