Research on digital art generation and cultural identity based on deep learning algorithms

Junhao Zhang

doi:10.1117/12.3046074

17 October 2024 Research on digital art generation and cultural identity based on deep learning algorithms

Junhao Zhang

Proceedings Volume 13289, International Conference on Optical Communication and Optoelectronic Technology (OCOT 2024); 132890V (2024) https://doi.org/10.1117/12.3046074
Event: The International Conference on Optical Communication and Optoelectronic Technology (OCOT 2024), 2024, Hangzhou, China

Abstract

In order to explore the application of deep learning technology in digital art creation, this study uses Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Style Transfer techniques to analyze their ability to imitate and innovate artistic styles and their impact on cultural identity. The results indicate that these models can effectively generate images with high artistic and cultural expression, providing a new perspective for the integration of art and technology in the future.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Junhao Zhang "Research on digital art generation and cultural identity based on deep learning algorithms", Proc. SPIE 13289, International Conference on Optical Communication and Optoelectronic Technology (OCOT 2024), 132890V (17 October 2024); https://doi.org/10.1117/12.3046074

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