Learning with noisy labels

Learning with noisy labels means When we say "noisy labels," we mean that an adversary has intentionally messed up the labels, which would have come from a "clean" distribution otherwise. This setting can also be used to cast learning from only positive and unlabeled data.

Papers

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Showing 76–100 of 249 papers

Title	Date	Tasks	Status	Hype	Score
ProMix: Combating Label Noise via Maximizing Clean Sample Utility	Jul 21, 2022	Learning with noisy labels	CodeCode Available	1	5
Combating noisy labels by agreement: A joint training method with co-regularization	Mar 5, 2020	DiversityImage Classification	CodeCode Available	1	5
Provably End-to-end Label-Noise Learning without Anchor Points	Feb 4, 2021	Learning with noisy labels	CodeCode Available	1	5
Faster Meta Update Strategy for Noise-Robust Deep Learning	Apr 30, 2021	Deep LearningImage Classification	CodeCode Available	1	5
L2B: Learning to Bootstrap Robust Models for Combating Label Noise	Feb 9, 2022	Image ClassificationImage Segmentation	CodeCode Available	1	5
Robust Training under Label Noise by Over-parameterization	Feb 28, 2022	Learning with noisy labels	CodeCode Available	1	5
RONO: Robust Discriminative Learning With Noisy Labels for 2D-3D Cross-Modal Retrieval	Jan 1, 2023	Cross-Modal RetrievalLearning with noisy labels	CodeCode Available	1	5
FedNoisy: Federated Noisy Label Learning Benchmark	Jun 20, 2023	Federated LearningLearning with noisy labels	CodeCode Available	1	5
Dimensionality-Driven Learning with Noisy Labels	Jun 7, 2018	Image ClassificationLearning with noisy labels	CodeCode Available	0	5
Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels	May 19, 2025	Learning with noisy labelsNoise Estimation	CodeCode Available	0	5
CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy Labels	Jul 16, 2025	Learning with noisy labelsMeta-Learning	CodeCode Available	0	5
Model and Data Agreement for Learning with Noisy Labels	Dec 2, 2022	Learning with noisy labels	CodeCode Available	0	5
Mitigating Label Noise on Graph via Topological Sample Selection	Mar 4, 2024	Learning with noisy labels	CodeCode Available	0	5
NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in Natural Language Processing	May 18, 2023	Learning with noisy labels	CodeCode Available	0	5
Debiased Sample Selection for Combating Noisy Labels	Jan 24, 2024	Learning with noisy labels	CodeCode Available	0	5
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach	Sep 13, 2016	DiversityImage Classification	CodeCode Available	0	5
Cross-to-merge training with class balance strategy for learning with noisy labels	Apr 1, 2024	Image ClassificationLearning with noisy labels	CodeCode Available	0	5
LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment	Mar 6, 2021	Image ClassificationLearning with noisy labels	CodeCode Available	0	5
Meta Label Correction for Noisy Label Learning	Nov 10, 2019	Image ClassificationLearning with noisy labels	CodeCode Available	0	5
Can We Treat Noisy Labels as Accurate?	May 21, 2024	Learning with noisy labels	CodeCode Available	0	5
Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation Space	Jan 19, 2025	Contrastive LearningLearning with noisy labels	CodeCode Available	0	5
Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial	Jul 13, 2021	FairnessInformativeness	CodeCode Available	0	5
LNL+K: Enhancing Learning with Noisy Labels Through Noise Source Knowledge Integration	Jun 20, 2023	Learning with noisy labels	CodeCode Available	0	5
MILD: Modeling the Instance Learning Dynamics for Learning with Noisy Labels	Jun 20, 2023	Learning with noisy labelsMemorization	CodeCode Available	0	5
No Regret Sample Selection with Noisy Labels	Mar 6, 2020	Learning with noisy labels	CodeCode Available	0	5

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