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Datasets › CIFAR-10N

CIFAR-10N (Real-World Human Annotations)

Introduced by Jiaheng Wei et al. in Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations22 Oct 2021 archive 2025-07-28

This work presents two new benchmark datasets (CIFAR-10N, CIFAR-100N), equipping the training dataset of CIFAR-10 and CIFAR-100 with human-annotated real-world noisy labels that we collect from Amazon Mechanical Turk.

HomepageBenchmarksPapers JSON

Benchmarks archive 2025-07-28

All 6 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

First row (archive order)PaperCode
Learning with noisy labels CIFAR-10N-Aggregate ProMix Accuracy (mean) 97.39 ProMix: Combating Label Noise via Maximizing Clean Sample Utility justherozen/promix 26 Compare
Learning with noisy labels CIFAR-10N-Worst ProMix Accuracy (mean) 96.16 ProMix: Combating Label Noise via Maximizing Clean Sample Utility justherozen/promix 25 Compare
Learning with noisy labels CIFAR-10N-Random1 ProMix Accuracy (mean) 96.97 ProMix: Combating Label Noise via Maximizing Clean Sample Utility justherozen/promix 24 Compare
Learning with noisy labels CIFAR-10N-Random2 PSSCL Accuracy (mean) 96.21 PSSCL: A progressive sample selection framework with... LanXiaoPang613/PSSCL 23 Compare
Learning with noisy labels CIFAR-10N-Random3 PSSCL Accuracy (mean) 96.49 PSSCL: A progressive sample selection framework with... LanXiaoPang613/PSSCL 23 Compare
Learning with noisy labels CIFAR-10N ProMix Accuracy 97.39 ProMix: Combating Label Noise via Maximizing Clean Sample Utility justherozen/promix 1 Compare

Papers archive 2025-07-28

23 shown of 23 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 97. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labels 1 5 18 Dec 2024 not harvested
Partial Label Supervision for Agnostic Generative Noisy Label Learning 1 5 2 Aug 2023 not harvested
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations 1 5 22 May 2023 not harvested
ProMix: Combating Label Noise via Maximizing Clean Sample Utility 1 4 21 Jul 2022 ran 0 of 1 samples (1 unverified)
Robust Training under Label Noise by Over-parameterization 1 5 28 Feb 2022 ran 1 of 8 samples (7 unverified)
Sample Prior Guided Robust Model Learning to Suppress Noisy Labels 1 3 2 Dec 2021 not harvested
Understanding Generalized Label Smoothing when Learning with Noisy Labels 1 1 29 Sep 2021 not harvested
Understanding and Improving Early Stopping for Learning with Noisy Labels 1 1 30 Jun 2021 not harvested
To Smooth or Not? When Label Smoothing Meets Noisy Labels 1 4 8 Jun 2021 ran 3 of 3 samples (0 unverified; 3 pointer-only for licence)
Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels 2 5 10 Feb 2021 ran 0 of 9 samples (9 unverified)
Provably End-to-end Label-Noise Learning without Anchor Points 1 5 4 Feb 2021 not harvested
When Optimizing f-divergence is Robust with Label Noise 2 5 7 Nov 2020 not harvested
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach 1 10 5 Oct 2020 ran 2 of 4 samples (2 unverified; 4 pointer-only for licence)
Early-Learning Regularization Prevents Memorization of Noisy Labels 2 10 30 Jun 2020 ran 1 of 1 samples (0 unverified)
Does label smoothing mitigate label noise? 0 5 5 Mar 2020 not harvested
Combating noisy labels by agreement: A joint training method with co-regularization 2 5 5 Mar 2020 not harvested
DivideMix: Learning with Noisy Labels as Semi-supervised Learning 2 5 18 Feb 2020 ran 0 of 2 samples (2 unverified; 2 pointer-only for licence)
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates 2 5 8 Oct 2019 ran 4 of 6 samples (2 unverified)
Are Anchor Points Really Indispensable in Label-Noise Learning? 1 5 1 Jun 2019 ran 3 of 3 samples (0 unverified; 3 pointer-only for licence)
How does Disagreement Help Generalization against Label Corruption? 3 5 14 Jan 2019 not harvested
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels 4 5 20 May 2018 not harvested
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels 5 5 18 Apr 2018 ran 7 of 7 samples (0 unverified; 7 pointer-only for licence)
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach 2 10 13 Sep 2016 ran 2 of 2 samples (0 unverified; 2 pointer-only for licence)

Dataset loaders archive 2025-07-28

torch-uncertainty.github.io/ensta-u2is/torch-uncertaintypytorch
zwzhu-d/cifar-10-100npytorch

2 loaders as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

Image ClassificationLearning with noisy labels

License archive 2025-07-28

No licence recorded in the archive. Absence here is not a statement about the dataset's terms.

Modalities archive 2025-07-28

No modality tagged.

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

  • CIFAR-10N
  • CIFAR-10N-Aggregate
  • CIFAR-10N-Random1
  • CIFAR-10N-Random2
  • CIFAR-10N-Random3
  • CIFAR-10N-Worst

6 variant names, as the archive lists them.

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