Target-Embedding Autoencoder With Knowledge Distillation for Multi-Label Classification-Reference-Cited by-同舟云学术

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Target-Embedding Autoencoder With Knowledge Distillation for Multi-Label Classification

Published:2024-06 Issue:3 Volume:8 Page:2506-2517
ISSN:2471-285X
Container-title:IEEE Transactions on Emerging Topics in Computational Intelligence
language:
Short-container-title:IEEE Trans. Emerg. Top. Comput. Intell.

Author:

Ma Ying¹^ORCID,Zou Xiaoyan²^ORCID,Pan Qizheng²^ORCID,Yan Ming³^ORCID,Li Guoqi⁴^ORCID

Affiliation:

1. Faculty of Computing, Harbin Institute of Technology, Harbin, China

2. Xiamen University of Technology, Xiamen, China

3. Institute of High-Performance Computing, Singapore Science and Technology Bureau, Singapore

4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

Funder

Fujian Provincial Science Foundation for Outstanding Youth

National Key R&D Plan

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7433297/10538459/10477613.pdf?arnumber=10477613

Reference41 articles.

1. Metric Learning for Multi-Output Tasks

2. An easy-to-hard learning paradigm for multiple classes and multiple labels;Liu;J. Mach. Learn. Res.,2017

3. Multi-label prediction via compressed sensing;Hsu,2009

4. Multilabel Classification with Principal Label Space Transformation

5. Multi-Label output codes using canonical correlation analysis;Zhang,2011

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