OpenCodePapers
learning-with-noisy-labels-on-cifar-10n
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Learning with noisy labels
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Paper
Code
Accuracy (mean)
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ModelName
ReleaseDate
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ProMix: Combating Label Noise via Maximizing Clean Sample Utility
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97.39
ProMix
2022-07-21
PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labels
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96.41
PSSCL
2024-12-18
Sample Prior Guided Robust Model Learning to Suppress Noisy Labels
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96.11
PGDF
2021-12-02
Robust Training under Label Noise by Over-parameterization
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95.61
SOP+
2022-02-28
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
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95.47
ILL
2023-05-22
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
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95.25
CORES*
2020-10-05
DivideMix: Learning with Noisy Labels as Semi-supervised Learning
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95.01
Divide-Mix
2020-02-18
Early-Learning Regularization Prevents Memorization of Noisy Labels
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94.83
ELR+
2020-06-30
Understanding and Improving Early Stopping for Learning with Noisy Labels
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94.66
PES (Semi)
2021-06-30
Partial Label Supervision for Agnostic Generative Noisy Label Learning
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92.57
GNL
2023-08-02
Early-Learning Regularization Prevents Memorization of Noisy Labels
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92.38
ELR
2020-06-30
Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels
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91.97
CAL
2021-02-10
To Smooth or Not? When Label Smoothing Meets Noisy Labels
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91.97
Negative-LS
2021-06-08
When Optimizing $f$-divergence is Robust with Label Noise
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91.64
F-div
2020-11-07
Does label smoothing mitigate label noise?
91.57
Positive-LS
2020-03-05
Combating noisy labels by agreement: A joint training method with co-regularization
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91.44
JoCoR
2020-03-05
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
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91.23
CORES
2020-10-05
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
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91.20
Co-Teaching
2018-04-18
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates
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90.75
Peer Loss
2019-10-08
How does Disagreement Help Generalization against Label Corruption?
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90.61
Co-Teaching+
2019-01-14
Provably End-to-end Label-Noise Learning without Anchor Points
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89.70
VolMinNet
2021-02-04
Are Anchor Points Really Indispensable in Label-Noise Learning?
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88.52
T-Revision
2019-06-01
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
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88.24
Forward-T
2016-09-13
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
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88.13
Backward-T
2016-09-13
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
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87.85
GCE
2018-05-20
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87.77
CE