Low-Rank Correlation Learning for Unsupervised Domain Adaptation-Reference-Cited by-同舟云学术

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Low-Rank Correlation Learning for Unsupervised Domain Adaptation

Published:2023 Issue: Volume: Page:1-14
ISSN:1520-9210
Container-title:IEEE Transactions on Multimedia
language:
Short-container-title:IEEE Trans. Multimedia

Author:

Lu Yuwu¹,Wong Wai Keung²,Yuan Chun³,Lai Zhihui⁴,Li Xuelong⁵

Affiliation:

1. School of Software, South China Normal University, Foshan, China

2. Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong

3. Tsinghua Shenzhen International Graduate School and Peng Cheng Laboratory, Shenzhen, China

4. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

5. School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an, China

Funder

National Natural Science Foundation of China

Basic and Applied Basic Research Foundation of Guangdong Province

Laboratory for Artificial Intelligence in Design

Innovation and Technology Fund, Hong Kong SAR

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Media Technology,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6046/4456689/10269005.pdf?arnumber=10269005

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. ACAN: A plug-and-play Adaptive Center-Aligned Network for unsupervised domain adaptation;Engineering Applications of Artificial Intelligence;2024-11

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