Year: 2024
Type: article
Abstract: With the rapid development of deep learning technology, style transfer has achieved significant breakthroughs at both algorithmic and application levels, greatly promoting innovative interactions between content and style. This paper comprehensively reviews the fundamental concepts, classifications, and widespread applications of style transfer within neural networks, with a particular focus on the principles, diverse variants, and synthesis techniques of neural network-based style transfer. Additionally, the article explores image style transfer as a research hotspot in the field of artificial intelligence and computer vision, highlighting that traditional methods primarily rely on physical and texture synthesis techniques, which often result in coarse outcomes and poor robustness. Through comparative analysis, this paper outlines the development trajectory and latest research advances in image style transfer and proposes future research directions.
DOI: 10.54097/mxgtcj89
Author: Mingyu Huang
Institution: Northwestern Polytechnical University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4405808671