OpenCodePapers

learning-with-noisy-labels-on-cifar-100n

Document Text ClassificationLearning with noisy labels
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PaperCodeAccuracy (mean)ModelNameReleaseDate
Sample Prior Guided Robust Model Learning to Suppress Noisy Labels✓ Link74.08PGDF2021-12-02
ProMix: Combating Label Noise via Maximizing Clean Sample Utility✓ Link73.39ProMix2022-07-21
PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labels✓ Link72.00PSSCL2024-12-18
DivideMix: Learning with Noisy Labels as Semi-supervised Learning✓ Link71.13Divide-Mix2020-02-18
Robust Training under Label Noise by Over-parameterization✓ Link67.81SOP+2022-02-28
Early-Learning Regularization Prevents Memorization of Noisy Labels✓ Link66.72ELR+2020-06-30
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations✓ Link65.84ILL2023-05-22
Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels✓ Link61.73CAL2021-02-10
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach✓ Link61.15CORES2020-10-05
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels✓ Link60.37Co-Teaching2018-04-18
Combating noisy labels by agreement: A joint training method with co-regularization✓ Link59.97JoCoR2020-03-05
Early-Learning Regularization Prevents Memorization of Noisy Labels✓ Link58.94ELR2020-06-30
To Smooth or Not? When Label Smoothing Meets Noisy Labels✓ Link58.59Negative-LS2021-06-08
How does Disagreement Help Generalization against Label Corruption?✓ Link57.88Co-Teaching+2019-01-14
Provably End-to-end Label-Noise Learning without Anchor Points✓ Link57.80VolMinNet2021-02-04
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates✓ Link57.59Peer Loss2019-10-08
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach✓ Link57.14Backward-T2016-09-13
When Optimizing $f$-divergence is Robust with Label Noise✓ Link57.10F-div2020-11-07
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach✓ Link57.01Forward-T2016-09-13
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels✓ Link56.73GCE2018-05-20
Does label smoothing mitigate label noise?55.84Positive-LS2020-03-05
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach✓ Link55.72CORES*2020-10-05
[]()55.50CE
Are Anchor Points Really Indispensable in Label-Noise Learning?✓ Link51.55T-Revision2019-06-01
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