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

Learning with noisy labels

Results over time

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Leaderboard

Paper	Code	Accuracy (mean)	ModelName	ReleaseDate
Sample Prior Guided Robust Model Learning to Suppress Noisy Labels	✓ Link	74.08	PGDF	2021-12-02
ProMix: Combating Label Noise via Maximizing Clean Sample Utility	✓ Link	73.39	ProMix	2022-07-21
PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labels	✓ Link	72.00	PSSCL	2024-12-18
DivideMix: Learning with Noisy Labels as Semi-supervised Learning	✓ Link	71.13	Divide-Mix	2020-02-18
Robust Training under Label Noise by Over-parameterization	✓ Link	67.81	SOP+	2022-02-28
Early-Learning Regularization Prevents Memorization of Noisy Labels	✓ Link	66.72	ELR+	2020-06-30
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations	✓ Link	65.84	ILL	2023-05-22
Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels	✓ Link	62.17	ANTIDOTE	2025-08-08
Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels	✓ Link	61.73	CAL	2021-02-10
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach	✓ Link	61.15	CORES	2020-10-05
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels	✓ Link	60.37	Co-Teaching	2018-04-18
Combating noisy labels by agreement: A joint training method with co-regularization	✓ Link	59.97	JoCoR	2020-03-05
Early-Learning Regularization Prevents Memorization of Noisy Labels	✓ Link	58.94	ELR	2020-06-30
To Smooth or Not? When Label Smoothing Meets Noisy Labels	✓ Link	58.59	Negative-LS	2021-06-08
How does Disagreement Help Generalization against Label Corruption?	✓ Link	57.88	Co-Teaching+	2019-01-14
Provably End-to-end Label-Noise Learning without Anchor Points	✓ Link	57.80	VolMinNet	2021-02-04
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates	✓ Link	57.59	Peer Loss	2019-10-08
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach	✓ Link	57.14	Backward-T	2016-09-13
When Optimizing $f$-divergence is Robust with Label Noise	✓ Link	57.10	F-div	2020-11-07
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach	✓ Link	57.01	Forward-T	2016-09-13
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels	✓ Link	56.73	GCE	2018-05-20
Does label smoothing mitigate label noise?		55.84	Positive-LS	2020-03-05
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach	✓ Link	55.72	CORES*	2020-10-05
		55.50	CE
Are Anchor Points Really Indispensable in Label-Noise Learning?	✓ Link	51.55	T-Revision	2019-06-01

OpenCodePapers

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