learning-with-noisy-labels-on-cifar-10n-aggregate

Learning with noisy labels

Results over time

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Leaderboard

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

OpenCodePapers

learning-with-noisy-labels-on-cifar-10n-aggregate