Loading [MathJax]/extensions/MathMenu.js
Image Style Transfer Using Deep Learning Methods | IEEE Conference Publication | IEEE Xplore

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 anoth...Show More

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.
Date of Conference: 25-27 February 2022
Date Added to IEEE Xplore: 06 April 2022
ISBN Information:
Conference Location: Changchun, China

Contact IEEE to Subscribe

References

References is not available for this document.