BadLabel: A Robust Perspective on Evaluating and Enhancing Label-Noise Learning-Reference-Cited by-同舟云学术

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BadLabel: A Robust Perspective on Evaluating and Enhancing Label-Noise Learning

Published:2024-06 Issue:6 Volume:46 Page:4398-4409
ISSN:0162-8828
Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
language:
Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

Zhang Jingfeng¹^ORCID,Song Bo²^ORCID,Wang Haohan³^ORCID,Han Bo⁴^ORCID,Liu Tongliang⁵^ORCID,Liu Lei²^ORCID,Sugiyama Masashi⁶^ORCID

Affiliation:

1. University of Auckland, Auckland, New Zealand

2. Shandong University, Jinan, China

3. University of Illinois Urbana–Champaign, Champaign, IL, USA

4. Hong Kong Baptist University, Kowloon, Hong Kong

5. University of Sydney, Camperdown, NSW, Australia

6. RIKEN, AIP, Tokyo, Japan

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shandong Province

Taishan Scholars

RGC Early Career Scheme

NSFC General

Basic and Applied Basic Research Foundation of Guangdong Province

HKBU Faculty Niche Research Areas

HKBU CSD Departmental Incentive Scheme

Australian Research Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/34/10522060/10404058.pdf?arnumber=10404058

Reference57 articles.

1. Learning from multiple annotators with varying expertise

2. Webvision database: Visual learning and understanding from web data;Li,2017

3. Learning with Biased Complementary Labels

4. Learning with noisy labels;Natarajan

5. Learning from noisy examples

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1. Lightweight Deep Learning Model Optimization for Medical Image Analysis;International Journal of Imaging Systems and Technology;2024-09

2. Contrastive Learning Joint Regularization for Pathological Image Classification with Noisy Labels;Electronics;2024-06-22

3. Chance-Constrained Abnormal Data Cleaning for Robust Classification With Noisy Labels;IEEE Transactions on Emerging Topics in Computational Intelligence;2024

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