Abstract: Deep learning and art design are being integrated, which is an innovative process that has the potential to reframe the way the human imagination is defined. This paper is an exploration of a broad field that showcases how AI enhances the experience of artist practice, especially content deep learning. This study comprises an exhaustive analysis of the cutting-edge models including generative adversarial networks (GANs), neural style transfer, and multimodal AI that assist in the creation, modification, and optimization of the artistic experience. This research points to implementations of those in the visual arts, graphic design, and interactive media while providing contemporary examples where deep learning has been an addition to traditional media and created new forms of art. Besides, the paper points to the challenges and ethical considerations concerning algorithmic art, including issues of authorship, biases, and intellectual property. The integration of computational methods in the realm of artistic expression is made in the paper and the paper provides insights into the change that deep learning can affect for artists, designers, and technologists.

Yanjie Deng and Qibing Zhai, “Integrating Deep Learning in Art and Design: Computational Techniques for Enhancing Creative Expression” International Journal of Advanced Computer Science and Applications(IJACSA), 16(2), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160218

@article{Deng2025,
title = {Integrating Deep Learning in Art and Design: Computational Techniques for Enhancing Creative Expression},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160218},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160218},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {2},
author = {Yanjie Deng and Qibing Zhai}
}