ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows

Universal style transfer retains styles from reference images in content images. While existing methods have achieved state-of-the-art style transfer performance, they are not aware of the content leak phenomenon that the image content may corrupt after several rounds of stylization process. In this...

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Bibliographic Details

Published in:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) pp. 862 - 871
Main Authors:	An, Jie, Huang, Siyu, Song, Yibing, Dou, Dejing, Liu, Wei, Luo, Jiebo
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.06.2021
Subjects:	Computer vision Feature extraction Inference algorithms Neural networks Pattern recognition
ISSN:	1063-6919
Online Access:	Get full text
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