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Learning from Noisy Labels with Decoupled Meta Label Purifier

Learning from Noisy Labels with Decoupled Meta Label Purifier

14 February 2023
Yuanpeng Tu
Boshen Zhang
Yuxi Li
Liang Liu
Jian Li
Yabiao Wang
Chengjie Wang
C. Zhao
    NoLa
ArXivPDFHTML

Papers citing "Learning from Noisy Labels with Decoupled Meta Label Purifier"

15 / 15 papers shown
Title
Set a Thief to Catch a Thief: Combating Label Noise through Noisy Meta Learning
Set a Thief to Catch a Thief: Combating Label Noise through Noisy Meta Learning
Hanxuan Wang
Na Lu
Xueying Zhao
Yuxuan Yan
Kaipeng Ma
Kwoh Chee Keong
Gustavo Carneiro
NoLa
59
0
0
22 Feb 2025
Combating Semantic Contamination in Learning with Label Noise
Combating Semantic Contamination in Learning with Label Noise
Wenxiao Fan
Kan Li
NoLa
172
0
0
16 Dec 2024
Meta-Learn Unimodal Signals with Weak Supervision for Multimodal
  Sentiment Analysis
Meta-Learn Unimodal Signals with Weak Supervision for Multimodal Sentiment Analysis
Sijie Mai
Yu Zhao
Ying Zeng
Jianhua Yao
Haifeng Hu
28
2
0
28 Aug 2024
Foster Adaptivity and Balance in Learning with Noisy Labels
Foster Adaptivity and Balance in Learning with Noisy Labels
Mengmeng Sheng
Zeren Sun
Tao Chen
Shuchao Pang
Yucheng Wang
Yazhou Yao
31
2
0
03 Jul 2024
A Multi-module Robust Method for Transient Stability Assessment against
  False Label Injection Cyberattacks
A Multi-module Robust Method for Transient Stability Assessment against False Label Injection Cyberattacks
Hanxuan Wang
Na Lu
Yinhong Liu
Zhuqing Wang
Zixuan Wang
AAML
29
0
0
10 Jun 2024
CoLafier: Collaborative Noisy Label Purifier With Local Intrinsic
  Dimensionality Guidance
CoLafier: Collaborative Noisy Label Purifier With Local Intrinsic Dimensionality Guidance
Dongyu Zhang
Ruofan Hu
Elke A. Rundensteiner
34
0
0
10 Jan 2024
De-Confusing Pseudo-Labels in Source-Free Domain Adaptation
De-Confusing Pseudo-Labels in Source-Free Domain Adaptation
I. Diamant
Amir Rosenfeld
Idan Achituve
Jacob Goldberger
Arnon Netzer
31
1
0
03 Jan 2024
Fine tuning Pre trained Models for Robustness Under Noisy Labels
Fine tuning Pre trained Models for Robustness Under Noisy Labels
Sumyeong Ahn
Sihyeon Kim
Jongwoo Ko
SeYoung Yun
AAML
NoLa
27
5
0
24 Oct 2023
Mitigating Label Noise through Data Ambiguation
Mitigating Label Noise through Data Ambiguation
Julian Lienen
Eyke Hüllermeier
NoLa
32
6
0
23 May 2023
Learning with Noisy Labels through Learnable Weighting and Centroid
  Similarity
Learning with Noisy Labels through Learnable Weighting and Centroid Similarity
F. Wani
Maria Sofia Bucarelli
Fabrizio Silvestri
NoLa
29
3
0
16 Mar 2023
Learning with Noisy Labels over Imbalanced Subpopulations
Learning with Noisy Labels over Imbalanced Subpopulations
Mingcai Chen
Yu Zhao
Bing He
Zongbo Han
Bingzhe Wu
Jianhua Yao
16
8
0
16 Nov 2022
Maximising the Utility of Validation Sets for Imbalanced Noisy-label
  Meta-learning
Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning
D. Hoang
Cuong C. Nguyen
Cuong Nguyen anh Belagiannis Vasileios
G. Carneiro
13
2
0
17 Aug 2022
L2B: Learning to Bootstrap Robust Models for Combating Label Noise
L2B: Learning to Bootstrap Robust Models for Combating Label Noise
Yuyin Zhou
Xianhang Li
Fengze Liu
Qingyue Wei
Xuxi Chen
Lequan Yu
Cihang Xie
M. Lungren
Lei Xing
NoLa
39
3
0
09 Feb 2022
Improved Baselines with Momentum Contrastive Learning
Improved Baselines with Momentum Contrastive Learning
Xinlei Chen
Haoqi Fan
Ross B. Girshick
Kaiming He
SSL
267
3,369
0
09 Mar 2020
Mean teachers are better role models: Weight-averaged consistency
  targets improve semi-supervised deep learning results
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen
Harri Valpola
OOD
MoMe
261
1,275
0
06 Mar 2017
1