Say No to Redundant Information: Unsupervised Redundant Feature Elimination for Active Learning-Reference-Cited by-同舟云学术

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Say No to Redundant Information: Unsupervised Redundant Feature Elimination for Active Learning

Published:2024 Issue: Volume:26 Page:7721-7733
ISSN:1520-9210
Container-title:IEEE Transactions on Multimedia
language:
Short-container-title:IEEE Trans. Multimedia

Author:

Yang Jiachen¹^ORCID,Ma Shukun¹^ORCID,Zhang Zhuo¹^ORCID,Li Yang¹^ORCID,Xiao Shuai¹^ORCID,Wen JiaBao¹^ORCID,Lu Wen²^ORCID,Gao Xinbo³^ORCID

Affiliation:

1. School of Electrical and Information Engineering, Tianjin University, Tianjin, China

2. School of Electronic Engineering, Xidian University, Xian, China

3. State Key Laboratory of Integrated Services Networks, School of Electronic Engineering, Xidian University, Xi'an, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6046/10384483/10474184.pdf?arnumber=10474184

Reference66 articles.

1. ImageNet: A large-scale hierarchical image database

2. Deep Residual Learning for Image Recognition

3. ImageNet classification with deep convolutional neural networks;Krizhevsky,2012

4. No-reference quality index of tone-mapped images based on authenticity, preservation, and scene expressiveness

5. Quality Index for Stereoscopic Images by Jointly Evaluating Cyclopean Amplitude and Cyclopean Phase

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. Curvature index of image samples used to evaluate the interpretability informativeness;Engineering Applications of Artificial Intelligence;2024-11

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