A Study on the Effectiveness of Deep Learning Architectures in Style Transfer: A Comparative Analysis of CNN, VGG16, and VGG19

This study explores the performance differences among different deep learning models, including VGG19, VGG16, and a basic CNN, in the context of image style transfer. Style transfer is an image processing technique aimed at transferring the artistic style of one image onto the content of another. Our research motivation stems from the potential applications of style transfer across various domains and the opportunities for enhancing model performance. Through a series of experiments, we assess the performance of these models in terms of the visual quality of generated images, style preservation, feature extraction, and training efficiency. The results demonstrate significant variations in performance across diverse and complex conditions, especially in tasks involving advanced features. VGG19 and VGG16 exhibit exceptional performance, accurately capturing and conveying high-level features, resulting in the generation of synthesized images with artistic value. However, for tasks emphasizing speed and resource efficiency, the basic CNN also exhibits notable advantages. Lastly, we discuss the limitations of the study and provide suggestions for future work to further optimize model performance. In conclusion, the choice of an appropriate deep learning model should be determined by the nature and requirements of the task. This research provides valuable insights into the applications of different models in the field of image style transfer.