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Generative AI Model for Artistic Style Transfer Using Convolutional Neural Networks

Author

Listed:
  • Jonayet Miah

    (Department of Computer Science, University of South Dakota, South Dakota, USA)

  • Duc Minh Cao

    (Department of Economics, University of Tennessee, Knoxville, TN, USA)

  • Md Abu Sayed

    (Department of Professional Security Studies, New Jersey City University, Jersey City, New Jersey, USA)

  • Md Sabbirul Haque

    (IEEE Professional Community, IEEE)

  • Rhine Hoque

    (Researcher, Dhaka, Bangladesh)

Abstract

Artistic style transfer, a captivating application of generative artificial intelligence, involves fusing the content of one image with the artistic style of another to create unique visual compositions. This paper presents a comprehensive overview of a novel technique for style transfer using Convolutional Neural Networks (CNNs). By leveraging deep image representations learned by CNNs, we demonstrate how to separate and manipulate image content and style, enabling the synthesis of high-quality images that combine content and style in a harmonious manner. We describe the methodology, including content and style representations, loss computation, and optimization, and showcase experimental results highlighting the effectiveness and versatility of the approach across different styles and content.

Suggested Citation

  • Jonayet Miah & Duc Minh Cao & Md Abu Sayed & Md Sabbirul Haque & Rhine Hoque, 2023. "Generative AI Model for Artistic Style Transfer Using Convolutional Neural Networks," Journal of Computer Science and Technology Studies, Al-Kindi Center for Research and Development, vol. 5(4), pages 78-85, October.
    • Handle: RePEc:alk:jcsts0:v:5:y:2023:i:4:id:6166
    DOI: 10.32996/jcsts.2023.5.4.9
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