Meta Network for Flow-Based Image Style Transfer
學年 113
學期 2
出版（發表）日期 2025-05-16
作品名稱 Meta Network for Flow-Based Image Style Transfer
作品名稱（其他語言）
著者 Yihjia Tsai；Hsiau-Wen Lin；Hwei Jen Lin；Chii-Jen Chen；Chen-Hsiang Yu
單位
出版者
著錄名稱、卷期、頁數 Electronics 2025, 14(10) p.2035
摘要 A style transfer aims to produce synthesized images that retain the content of one image while adopting the artistic style of another. Traditional style transfer methods often require training separate transformation networks for each new style, limiting their adaptability and scalability. To address this challenge, we propose a flow-based image style transfer framework that integrates Randomized Hierarchy Flow (RH Flow) and a meta network for adaptive parameter generation. The meta network dynamically produces the RH Flow parameters conditioned on the style image, enabling efficient and flexible style adaptation without retraining for new styles. RH Flow enhances feature interaction by introducing a random permutation of the feature sub-blocks before hierarchical coupling, promoting diverse and expressive stylization while preserving the content structure. Our experimental results demonstrate that Meta FIST achieves superior content retention, style fidelity, and adaptability compared to existing approaches.
關鍵字 meta learning; image style transfer; convolutional neural network; instance normalization; adversarial learning; flow-based model
語言 en_US
ISSN
期刊性質 國外
收錄於 SCI EI
產學合作
通訊作者 Hwei Jen Lin
審稿制度 否
國別 CHE
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/127370 )