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learning-with-noisy-labels-on-cifar-10n-2
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Learning with noisy labels
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Code
Accuracy (mean)
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ModelName
ReleaseDate
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PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labels
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96.21
PSSCL
2024-12-18
Robust Training under Label Noise by Over-parameterization
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95.31
SOP
2022-02-28
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
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95.04
ILL
2023-05-22
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
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94.88
CORES*
2020-10-05
Early-Learning Regularization Prevents Memorization of Noisy Labels
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94.20
ELR+
2020-06-30
Early-Learning Regularization Prevents Memorization of Noisy Labels
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91.61
ELR
2020-06-30
Partial Label Supervision for Agnostic Generative Noisy Label Learning
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91.42
GNL
2023-08-02
DivideMix: Learning with Noisy Labels as Semi-supervised Learning
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90.90
Divide-Mix
2020-02-18
Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels
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90.75
CAL
2021-02-10
To Smooth or Not? When Label Smoothing Meets Noisy Labels
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90.37
Negative-LS
2021-06-08
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
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90.30
Co-Teaching
2018-04-18
Combating noisy labels by agreement: A joint training method with co-regularization
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90.21
JoCoR
2020-03-05
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
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89.91
CORES
2020-10-05
When Optimizing $f$-divergence is Robust with Label Noise
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89.79
F-div
2020-11-07
How does Disagreement Help Generalization against Label Corruption?
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89.47
Co-Teaching+
2019-01-14
Does label smoothing mitigate label noise?
89.35
Positive-LS
2020-03-05
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates
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88.76
Peer Loss
2019-10-08
Provably End-to-end Label-Noise Learning without Anchor Points
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88.27
VolMinNet
2021-02-04
Are Anchor Points Really Indispensable in Label-Noise Learning?
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87.71
T-Revision
2019-06-01
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
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87.70
GCE
2018-05-20
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86.46
CE
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
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86.28
Backward-T
2016-09-13
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
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86.14
Forward-T
2016-09-13