Neural Style Transfer for Selective Feature Emphasis
Appikatla Vijay Kumar, J. A. M. Rexie · 2024
The Neural Style Transfer (NST) helps in highlighting important features that contributes to the development of machine models in the field of Machine Learning. While NST offers a powerful tool for creating artistic imagery, challenges remain in preserving content details and achieving a natural fusion of style and content. By utilizing VGG19 to extract content and stylistic elements, the suggested method resolves these problems. In order to effectively transfer creative styles while preserving the information and emphasizing aspects of the input image, a combined content and loss of style function was used to direct the process of optimization. The process of style transmission can be better understood by examining feature statistics and high-frequency component analyses. Furthermore, complete variation loss is used to assist avoid over-smoothing during optimization. The suggested approach successfully achieves style transfer while maintaining content and emphasizing features. Hence the work proposed in this article contributes to the advancement of NST by showcasing its capabilities, overcoming challenges in content preservation and feature emphasis, and outlining promising directions for responsible exploration and application.