A Deep Learning-Based Generative Adversarial Network for Digital Art Style Migration

This study introduces the ConvNeXt-CycleGAN, a novel deep learning-based Generative Adversarial Network (GAN) designed for digital art style migration. The model addresses the time-consuming and expertise-driven nature of traditional artistic creation, aiming to automate and accelerate the style transfer process using artificial intelligence. The ConvNeXt-CycleGAN integrates ConvNeXt blocks within the CycleGAN framework, enhancing convolution capabilities and leveraging self-attention mechanisms for precise and nuanced artistic style capture. The model undergoes rigorous evaluation using multiple performance metrics, including Inception Score (IS), Peak Signal-to-Noise Ratio (PSNR), and Fréchet Inception Distance (FID), ensuring its effectiveness in generating high-quality, diverse images while retaining fidelity during style transfer. The ConvNeXt-CycleGAN surpasses traditional GAN models across key metrics: it achieves an IS of 12.7004 (higher image diversity), a PSNR of 14.0211 (better preservation of original artwork integrity), and an FID of 234.1679 (closer resemblance to real artistic distributions). Additionally, its ability to efficiently train on unpaired images via unsupervised learning enhances its real-world applicability. This research presents an architectural innovation by combining ConvNeXt blocks with the CycleGAN framework, offering robust performance across diverse datasets and artistic styles. The ConvNeXt-CycleGAN represents a significant advancement in the integration of AI with creative processes, providing a powerful tool for rapid prototyping in digital art creation and innovation.

@article{Ou2025,
  title     = {A Deep Learning-Based Generative Adversarial Network for Digital Art Style Migration},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {3},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Wenting Ou},
  doi       = {10.14569/IJACSA.2025.0160363},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160363}
}