Applying deep learning for style transfer in digital art: enhancing creative expression through neural networks

Applying deep learning for style transfer in digital art: enhancing creative expression through neural networks

Abstract Neural style transfer (NST) has opened new possibilities for digital art by enabling the blending of distinct artistic styles with content from various images. However, traditional NST methods often need help balancing style fidelity and content preservation, and many models need more compu...

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Bibliographic Details
Main Authors: Shijun Zhang, Yanling Qi, Jingqi Wu
Format: Article
Language:English
Published: Nature Portfolio 2025-04-01
Series:Scientific Reports
Subjects:
Neural style transfer
Convolutional neural networks
Content preservation
Style fidelity
Adaptive instance normalization
Gram matrix
Online Access:https://doi.org/10.1038/s41598-025-95819-9
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