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Geometry Flow-Based Deep Riemannian Metric Learning

Yangyang Li Chaoqun Fei Chuanqing Wang Hongming Shan Ruqian Lu

自动化学报（英文版）2023，Vol.10Issue(9)：1882-1892,11.

自动化学报（英文版）2023，Vol.10Issue(9)：1882-1892,11.DOI:10.1109/JAS.2023.123399

Geometry Flow-Based Deep Riemannian Metric Learning

Yangyang Li ¹Chaoqun Fei ¹Chuanqing Wang ¹Hongming Shan ²Ruqian Lu¹

作者信息

1. Key Lab of MADIS Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 100190,China
2. Institute of Science and Technology for Brain-Inspired Intelligence and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence(Ministry of Education)and MOE Frontiers Center for Brain Science,Fudan University,Shanghai 200433,and also with the Shanghai Center for Brain Science and Brain-Inspired Technology,Shanghai 200031,China
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摘要

关键词

Curvature regularization/deep metric learning(DML)/embedding learning/geometry flow/riemannian metric

Key words

Curvature regularization/deep metric learning(DML)/embedding learning/geometry flow/riemannian metric

引用本文复制引用

Yangyang Li,Chaoqun Fei,Chuanqing Wang,Hongming Shan,Ruqian Lu..Geometry Flow-Based Deep Riemannian Metric Learning[J].自动化学报（英文版）,2023,10(9):1882-1892,11.

基金项目

This work was supported in part by the Young Elite Scientists Sponsorship Program by CAST(2022QNRC001),the Nati-onal Natural Science Foundation of China(61621003,62101136),Natural Science Foundation of Shanghai(21ZR1403600),Shanghai Municipal Science and Technology Major Project(2018SHZDZX01)and ZJLab,and Shanghai Municipal of Science and Technology Project(20JC1419500). （2022QNRC001）

自动化学报（英文版）

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ISSN：2329-9266

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