MIND-Net: A Deep Mutual Information Distillation Network for Realistic Low-Resolution Face Recognition-Reference-Cited by-同舟云学术

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MIND-Net: A Deep Mutual Information Distillation Network for Realistic Low-Resolution Face Recognition

Published:2021 Issue: Volume:28 Page:354-358
ISSN:1070-9908
Container-title:IEEE Signal Processing Letters
language:
Short-container-title:IEEE Signal Process. Lett.

Author:

Low Cheng-Yaw^ORCID,Teoh Andrew Beng-Jin^ORCID,Park Jaewoo

Funder

National Research Foundation of Korea

Ministry of Science and ICT

Korea government

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/97/9325893/09330619.pdf?arnumber=9330619

Cited by 18 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. Online Knowledge Distillation for Machine Health Prognosis Considering Edge Deployment;IEEE Internet of Things Journal;2024-08-15

2. A homogeneous low-resolution face recognition method using correlation features at the edge;Sensors and Systems for Space Applications XVII;2024-06-06

3. Descriptor Distillation: A Teacher-Student-Regularized Framework for Learning Local Descriptors;International Journal of Computer Vision;2024-04-24

4. CATFace: Cross-Attribute-Guided Transformer With Self-Attention Distillation for Low-Quality Face Recognition;IEEE Transactions on Biometrics, Behavior, and Identity Science;2024-01

5. Recognizability Embedding Enhancement for Very Low-Resolution Face Recognition and Quality Estimation;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06

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