Feature-Embedding Triplet Networks with a Separately Constrained Loss Function-Reference-Cited by-同舟云学术

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Feature-Embedding Triplet Networks with a Separately Constrained Loss Function

Published:2023-05-21 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Symposium on Circuits and Systems (ISCAS)
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Author:

Wang Ziheng¹,Niknia Farzad¹,Liu Shanshan²,Jiang Honglan³,Liu Siting⁴,Reviriego Pedro⁵,Lombardi Fabrizio¹

Affiliation:

1. Northeastern University,Department of Electrical and Computer Engineering,Boston,MA,USA

2. Klipsch School of Electrical and Computer Engineering, New Mexico State University,Las Cruces,NM,USA

3. Shanghai Jiao Tong University,Department of Micro-Nano Electronic,Shanghai,China

4. School of Information Science and Technilogy, Shanghai Tech University,Shanghai,China

5. Universidad Carlos III de Madrid,Departamento de Ingeniería Telemática,Madrid,Spain

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10181241/10181318/10181752.pdf?arnumber=10181752

Reference23 articles.

1. Deep unsupervised learning through spatial contrasting;hoffer;ArXiv Preprint,2016

2. Rethinking Triplet Loss for Domain Adaptation

3. ASAP7: A 7-nm finFET predictive process design kit

4. A Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks;chen;International Conference on Machine Learning,0

5. Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function

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