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Cited by 795Machine Learning and Data ClassificationAdversarial Robustness in Machine LearningData Quality and Management

Confident Learning: Estimating Uncertainty in Dataset Labels

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Northcutt, Curtis, Jiang, Lu, Chuang, Isaac·2021·Journal of Artificial Intelligence Research
DOI 10.1613/jair.1.12125
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Abstract

Learning exists in the context of data, yet notions of confidence typically focus on model predictions, not label quality. Confident learning (CL) is an alternative approach which focuses instead on label quality by characterizing and identifying label errors in datasets, based on the principles of pruning noisy data, counting with probabilistic thresholds to estimate noise, and ranking examples to train with confidence. Whereas numerous studies have developed these principles independently, here, we combine them, building on the assumption of a class-conditional noise process to directly estimate the joint distribution between noisy (given) labels and uncorrupted (unknown) labels. This results in a generalized CL which is provably consistent and experimentally performant. We present sufficient conditions where CL exactly finds label errors, and show CL performance exceeding seven recent competitive approaches for learning with noisy labels on the CIFAR dataset. Uniquely, the CL framework is not coupled to a specific data modality or model (e.g., we use CL to find several label errors in the presumed error-free MNIST dataset and improve sentiment classification on text data in Amazon Reviews). We also employ CL on ImageNet to quantify ontological class overlap (e.g., estimating 645 missile images are mislabeled as their parent class projectile), and moderately increase model accuracy (e.g., for ResNet) by cleaning data prior to training. These results are replicable using the open-source cleanlab release.

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  • Even in the most common non-medical ML datasets there is a significant percentage of wrong labels.

    “Even in the most common (non-medical) datasets of ML (e.g., MNIST208, CIFAR-100209, Fashion-MNIST210) there is a significant percentage of wrong labels211,212.”

    From The METRIC-framework for assessing data quality for trustworthy AI in medicine: A systematic review (2024) · cites this paper as [212]

    Section 5.4 reports blatant label errors in the original MNIST dataset, supporting the claim that a common non-medical benchmark contains wrong labels. It does not establish a significant percentage of wrong labels in MNIST, CIFAR-100, or Fashion-MNIST; Section 5 describes the CIFAR experiments as using noisy examples rather than documenting that rate in the original dataset. Page numbers are not available in our copy. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Northcutt, C., Jiang, L., & Chuang, I.
Affiliations
  • Massachusetts Institute of Technology (US)
  • Google (United States) (US)
ORCID
Venue
Journal of Artificial Intelligence Research
Publisher
AI Access Foundation
Published
2021-04-14
Volume
70
Pages
1373-1411
ISSN
1076-9757 (ISSN-L)
DOI
10.1613/jair.1.12125
OpenAlex
W2988175183
Cited by
795
References
4
Citations by year
2026: 180 · 2025: 157 · 2024: 139 · 2023: 128 · 2022: 101 · 2021: 67 · 2020: 22 · 2019: 1
FWCI
38.28
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Artificial Intelligence Research (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
Keywords
confident learning, label noise, label error detection, label quality, probabilistic thresholds, label ranking, sentiment classification, image classification

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