Transfer Learning in Image Style Transfer: Applications in Computer Graphics

Tinglei Zhu

doi:10.54254/2755-2721/2025.PO25232

Applied and Computational EngineeringOpen access

Transfer Learning in Image Style Transfer: Applications in Computer Graphics

Research Article

Open Access

Transfer Learning in Image Style Transfer: Applications in Computer Graphics

Tinglei Zhu ^1*

¹ School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

^*Corresponding author: zhutinglei496@gmail.com

Published on 20 July 2025

ACE Vol.178

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-285-0

ISBN (Online): 978-1-80590-286-7

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Abstract

Image style transfer has emerged as a fundamental technique in computer graphics and computer vision, enabling the transformation of visual content while preserving semantic information. The integration of transfer learning methodologies with style transfer frameworks has demonstrated significant improvements in computational efficiency, generalization capability, and quality enhancement across diverse application domains. This comprehensive review systematically analyzes the application of transfer learning techniques in image style transfer through three critical domains: artistic style transfer, photo-to-anime stylization, and medical image harmonization. Drawing upon a comprehensive review of key publications from 2016 to 2024, this paper establishes a taxonomy of transfer learning approaches in image style transfer. It evaluates their effectiveness across different application contexts and identifies fundamental principles underlying successful implementations. The analysis reveals that pre-trained feature representations reduce training time by 65-80% while maintaining comparable or superior quality metrics across all examined domains. The author proposes a unified evaluation framework for assessing transfer learning effectiveness and identifying critical research gaps requiring immediate attention. The findings provide actionable insights for researchers and practitioners, establishing clear guidelines for optimal transfer learning strategy selection based on domain characteristics, data availability, and computational constraints.

Keywords:

Transfer Learning, Image Style Transfer, Domain Adaptation, Generative Adversarial Networks, Medical Image Harmonization

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References

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References

[5]. F. Zhuang et al., "A Comprehensive Survey on Transfer Learning, " Proceedings of the IEEE, vol. 109, no. 1, pp. 43-76, Jan. 2021.

[6]. Y. Jing, Y. Yang, Z. Feng, J. Ye, and M. Song, "Neural Style Transfer: A Review, " IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 11, pp. 3365-3385, Nov. 2020.

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Cite this article

Zhu,T. (2025). Transfer Learning in Image Style Transfer: Applications in Computer Graphics. Applied and Computational Engineering,178,1-9.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-CDS 2025 Symposium: Data Visualization Methods for Evaluatio

ISBN: 978-1-80590-285-0(Print) / 978-1-80590-286-7(Online)

Editor: Marwan Omar, Elisavet Andrikopoulou

Conference website: https://2025.confcds.org/portsmouth.html

Conference date: 30 July 2025

Series: Applied and Computational Engineering

Volume number: Vol.178

ISSN: 2755-2721(Print) / 2755-273X(Online)

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