Deep Self-Supervised Diversity Promoting Learning on Hierarchical Hyperspheres for Regularization-Reference-Cited by-同舟云学术

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Deep Self-Supervised Diversity Promoting Learning on Hierarchical Hyperspheres for Regularization

Published:2023 Issue: Volume:11 Page:146208-146222
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Kim Youngsung¹^ORCID,Hyun Yoonsuk²^ORCID,Han Jae-Joon³,Yang Eunho⁴,Hwang Sung Ju⁴,Shin Jinwoo⁴^ORCID

Affiliation:

1. Department of Artificial Intelligence, Inha University, Incheon, South Korea

2. Department of Mathematics, Inha University, Incheon, South Korea

3. Samsung Advanced Institute of Technology (SAIT), Suwon, South Korea

4. Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea

Funder

Inha University Research Grant

Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant

Korea Government [Ministry of Science and ICT (MSIT)] [Artificial Intelligence Convergence Innovation Human Resources Development (Inha University)]

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10373009.pdf?arnumber=10373009

Reference32 articles.

1. Orthogonality-promoting distance metric learning: Convex relaxation and theoretical analysis;Xie

2. Learning latent space models with angular constraints;Xie

3. Support-vector networks

4. Learning towards minimum hyperspherical energy;Liu

5. Uncorrelation and evenness: A new diversity-promoting regularizer;Xie

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