Global Balanced Experts for Federated Long-Tailed Learning-Reference-Cited by-同舟云学术

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Global Balanced Experts for Federated Long-Tailed Learning

Published:2023-10-01 Issue: Volume:32 Page:4792-4802
ISSN:
Container-title:2023 IEEE/CVF International Conference on Computer Vision (ICCV)
language:
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Author:

Zeng Yaopei¹,Liu Lei¹,Liu Li²,Shen Li³,Liu Shaoguo⁴,Wu Baoyuan¹

Affiliation:

1. The Chinese University of Hong Kong,School of Data Science, Shenzhen Research Institute of Big Data,Shenzhen (CUHK-Shenzhen),China

2. Hong Kong University of Science and Technology (Guangzhou),Guangzhou, Guangdong,China

3. JD Explore Academy

4. Alibaba Group

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10376473/10376477/10378386.pdf?arnumber=10378386

Reference46 articles.

1. ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shot

2. Learning imbalanced datasets with label-distribution-aware margin loss;Cao;Advances in Neural Information Processing Systems,2019

3. SMOTE: Synthetic Minority Over-sampling Technique

4. Parametric Contrastive Learning

5. Class-Balanced Loss Based on Effective Number of Samples

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