A Parametrical Model for Instance-Dependent Label Noise-Reference-Cited by-同舟云学术

A Parametrical Model for Instance-Dependent Label Noise

Published:2023-12 Issue:12 Volume:45 Page:14055-14068
ISSN:0162-8828
Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
language:
Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

Yang Shuo¹^ORCID,Wu Songhua²^ORCID,Yang Erkun³^ORCID,Han Bo⁴^ORCID,Liu Yang⁵^ORCID,Xu Min⁶^ORCID,Niu Gang⁷^ORCID,Liu Tongliang²^ORCID

Affiliation:

1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

2. Trustworthy Machine Learning Lab, School of Computer Science, The University of Sydney, Camperdown, NSW, Australia

3. School of Electronic Engineering, Xidian University, Xi'an, China

4. Department of Computer Science, Hongkong Baptist University, Hong Kong, China

5. Department of Computer Science and Engineering, University of California Santa Cruz, Santa Cruz, CA, USA

6. School of Electrical and Data Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia

7. RIKEN center for Advanced Intelligence Project, Tokyo, Japan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

http://xplorestaging.ieee.org/ielx7/34/10308548/10209198.pdf?arnumber=10209198

Reference84 articles.

1. Searching to exploit memorization effect in learning with noisy labels;yao;Proc 37th Int Conf Mach Learn,2020

2. Dual T: Reducing estimation error for transition matrix in label-noise learning;yao;Proc 34th Conf Neural Inf Process Syst,2020

3. Learning from Noisy Labels with Distillation

4. Meta transition adaptation for robust deep learning with noisy labels;shu,2020

5. How does disagreement benefit co-teaching?;yu;Proc 36th Int Conf Mach Learn,2019

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Regularly Truncated M-Estimators for Learning With Noisy Labels;IEEE Transactions on Pattern Analysis and Machine Intelligence;2024

2. Cognition-Driven Structural Prior for Instance-Dependent Label Transition Matrix Estimation;IEEE Transactions on Neural Networks and Learning Systems;2024