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learning-with-noisy-labels-on-cifar-10n-worst
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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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ProMix: Combating Label Noise via Maximizing Clean Sample Utility
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96.16
ProMix
2022-07-21
PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labels
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95.12
PSSCL
2024-12-18
Sample Prior Guided Robust Model Learning to Suppress Noisy Labels
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93.65
PGDF
2021-12-02
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
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93.58
ILL
2023-05-22
Robust Training under Label Noise by Over-parameterization
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93.24
SOP+
2022-02-28
DivideMix: Learning with Noisy Labels as Semi-supervised Learning
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92.56
Divide-Mix
2020-02-18
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
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91.66
CORES*
2020-10-05
Early-Learning Regularization Prevents Memorization of Noisy Labels
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91.09
ELR+
2020-06-30
Partial Label Supervision for Agnostic Generative Noisy Label Learning
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86.99
GNL
2023-08-02
Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels
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85.36
CAL
2021-02-10
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
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83.83
Co-Teaching
2018-04-18
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
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83.60
CORES
2020-10-05
Early-Learning Regularization Prevents Memorization of Noisy Labels
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83.58
ELR
2020-06-30
Combating noisy labels by agreement: A joint training method with co-regularization
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83.37
JoCoR
2020-03-05
How does Disagreement Help Generalization against Label Corruption?
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83.26
Co-Teaching+
2019-01-14
Understanding Generalized Label Smoothing when Learning with Noisy Labels
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82.99
Negative-LS
2021-09-29
Does label smoothing mitigate label noise?
82.76
Positive-LS
2020-03-05
When Optimizing $f$-divergence is Robust with Label Noise
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82.53
F-div
2020-11-07
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates
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82.53
Peer Loss
2019-10-08
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
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80.66
GCE
2018-05-20
Provably End-to-end Label-Noise Learning without Anchor Points
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80.53
VolMinNet
2021-02-04
Are Anchor Points Really Indispensable in Label-Noise Learning?
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80.48
T-Revision
2019-06-01
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
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79.79
Forward-T
2016-09-13
[]()
77.69
CE
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
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77.61
Backward-T
2016-09-13