Transfer Learning in Image Style Transfer: Applications in Computer Graphics

Image style transfer has emerged as a fundamental technique in computer graphics and computer vision, enabling the transformation of visual content while preserving semantic information. The integration of transfer learning methodologies with style transfer frameworks has demonstrated significant improvements in computational efficiency, generalization capability, and quality enhancement across diverse application domains. This comprehensive review systematically analyzes the application of transfer learning techniques in image style transfer through three critical domains: artistic style transfer, photo-to-anime stylization, and medical image harmonization. Drawing upon a comprehensive review of key publications from 2016 to 2024, this paper establishes a taxonomy of transfer learning approaches in image style transfer. It evaluates their effectiveness across different application contexts and identifies fundamental principles underlying successful implementations. The analysis reveals that pre-trained feature representations reduce training time by 65-80% while maintaining comparable or superior quality metrics across all examined domains. The author proposes a unified evaluation framework for assessing transfer learning effectiveness and identifying critical research gaps requiring immediate attention. The findings provide actionable insights for researchers and practitioners, establishing clear guidelines for optimal transfer learning strategy selection based on domain characteristics, data availability, and computational constraints.