Image Style Transfer Using Deep Learning Methods

Abstract

Image style transfer is an increasingly popular technology that can learn the style of an existing picture through neural network algorithms and apply this style to another picture. It is widely used in the field of art, such as oil painting, cartoon animation production, image season conversion and text style conversion. Meanwhile, deep learning methods are attracting more and more attention both in research and applications in various areas. In this paper, we give an overview on current research progress and results of image style transfer using deep learning methods. The deep learning methods are categorized into Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN). As for CNN methods, we mainly talk about models based on VGG; and in terms of GAN methods, conditional GAN, Cycle GAN, and cartoon-GAN methods are contained. Finally, we summarized the shortcomings of the current results and the future study direction.