Holistic Label Correction for Noisy Multi-Label Classification-Reference-Cited by-同舟云学术

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Holistic Label Correction for Noisy Multi-Label Classification

Published:2023-10-01 Issue: Volume: Page:1483-1493
ISSN:
Container-title:2023 IEEE/CVF International Conference on Computer Vision (ICCV)
language:
Short-container-title:

Author:

Xia Xiaobo¹,Deng Jiankang²,Bao Wei¹,Du Yuxuan³,Han Bo⁴,Shan Shiguang⁵,Liu Tongliang¹

Affiliation:

1. The University of Sydney

2. Imperial College London

3. JD Explore Academy

4. Hong Kong Baptist University

5. Chinese Academy of Sciences

Funder

Young Scientists Fund

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10376473/10376477/10377582.pdf?arnumber=10377582

Reference78 articles.

1. A closer look at memorization in deep networks;Arpit

2. Deep k-nn for noisy labels;Bahri

3. Me-Momentum: Extracting Hard Confident Examples from Noisily Labeled Data

4. Confidence scores make instancedependent label-noise learning possible;Berthon,2020

5. Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition

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1. A ranking-based problem transformation method for weakly supervised multi-label learning;Pattern Recognition;2024-09

2. Decoding class dynamics in learning with noisy labels;Pattern Recognition Letters;2024-08

3. Like draws to like: A Multi-granularity Ball-Intra Fusion approach for fault diagnosis models to resists misleading by noisy labels;Advanced Engineering Informatics;2024-04

4. Robust Deep Neural Network for Learning in Noisy Multi-Label Food Images;Sensors;2024-03-22

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