A Comprehensive Study of Art Image Style Transfer Methods Based on Generative Adversarial Networks

Liyuan Huang, Hanlin Liu, Jiaqi Wen

2024

Abstract

Image style transfer is a cutting-edge technique that seamlessly merges one image's content with another's distinct style. The rapid progress of deep learning has led to significant advancements in image style transfer technology. Nevertheless, this technology still encounters several issues, such as the inability to attain the optimal expression effect of artistic attributes, and the mismatch between semantic and style characteristics. Based on the generative adversarial network (GAN), this paper examines the improved algorithmic applications of image style transfer technology in ink painting, animation, and oil painting. Additionally, using quantification and comparative analysis of the outcomes of the improved style transfer algorithm applied in diverse art forms, Foreseen are the obstacles to be tackled and the expected development path of image style transfer technology in the future. The application of image style transfer technology in the domain of art still demands more efficient algorithms and more artistic outputs. This study focuses on summarizing popular algorithms in image style transfer technology and driving forward innovation in style transfer techniques.

Paper Citation

in Harvard Style

Huang L., Liu H. and Wen J. (2024). A Comprehensive Study of Art Image Style Transfer Methods Based on Generative Adversarial Networks. In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-754-2, SciTePress, pages 171-178. DOI: 10.5220/0013512100004619

in Bibtex Style

@conference{daml24,
author={Liyuan Huang and Hanlin Liu and Jiaqi Wen},
title={A Comprehensive Study of Art Image Style Transfer Methods Based on Generative Adversarial Networks},
booktitle={Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2024},
pages={171-178},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013512100004619},
isbn={978-989-758-754-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - A Comprehensive Study of Art Image Style Transfer Methods Based on Generative Adversarial Networks
SN - 978-989-758-754-2
AU - Huang L.
AU - Liu H.
AU - Wen J.
PY - 2024
SP - 171
EP - 178
DO - 10.5220/0013512100004619
PB - SciTePress