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International Journal of New Developments in Engineering and Society, 2024, 8(1); doi: 10.25236/IJNDES.2024.080101.

Research on Image Style Transfer and Artistic Creation Algorithm Based on Generative Adversarial Networks


Liu Fenfen1, Zhu Zimin2

Corresponding Author:
Liu Fenfen

1Xi'an Peihua University, Xi'an, 710125, China

2Northeast Forestry University, Harbin, 150006, China


This paper presents a comprehensive exploration of image style transfer techniques and artistic creation algorithms utilizing Generative Adversarial Networks (GANs). The research is structured into three main sections. Firstly, an overview of traditional image style transfer techniques is provided, highlighting their strengths and limitations. Secondly, the fundamental concepts and architectures of GANs are discussed, elucidating their role in generating realistic and diverse images. Lastly, the fusion of GANs with image style transfer methodologies is examined, showcasing the synergistic potential of combining these two approaches for enhanced artistic creation. Through this interdisciplinary investigation, we aim to contribute to the advancement of computational creativity and visual aesthetics in the field of artificial intelligence.


Image style transfer, Generative adversarial networks, Artistic creation, Deep learning, Visual expression

Cite This Paper

Liu Fenfen, Zhu Zimin. Research on Image Style Transfer and Artistic Creation Algorithm Based on Generative Adversarial Networks. International Journal of New Developments in Engineering and Society (2024) Vol.8, Issue 1: 1-6. https://doi.org/10.25236/IJNDES.2024.080101.


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