Meta Network for Flow-Based Image Style Transfer

doi:10.3390/electronics14102035

淡江大學機構典藏 > 工學院 > 資訊工程學系暨研究所 > 期刊論文 > Item 987654321/127370

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题名:	Meta Network for Flow-Based Image Style Transfer
作者:	Yu, Yihjia Tsai；Hsiau-Wen Lin；Hwei Jen Lin；Chii-Jen Chen；Chen-Hsiang
关键词:	meta learning;image style transfer;convolutional neural network;instance normalization;adversarial learning;flow-based model
日期:	2025-05-16
上传时间:	2025-05-23 12:05:13 (UTC+8)
出版者:	Multidisciplinary Digital Publishing Institute
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.
關聯:	Electronics 2025, 14(10) p.2035
DOI:	10.3390/electronics14102035
显示于类别:	[資訊工程學系暨研究所] 期刊論文

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