mlsanityData to 7 Oct 2026

Addressing out-of-distribution label noise in webly-labelled data

arXiv 2110.13699 ↗First posted 26 Oct 2021cs.CVcited by 14

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Abstract (arXiv)

A recurring focus of the deep learning community is towards reducing the labeling effort. Data gathering and annotation using a search engine is a simple alternative to generating a fully human-annotated and human-gathered dataset. Although web crawling is very time efficient, some of the retrieved images are unavoidably noisy, i.e. incorrectly labeled. Designing robust algorithms for training on noisy data gathered from the web is an important research perspective that would render the building of datasets easier. In this paper we conduct a study to understand the type of label noise to expect when building a dataset using a search engine. We review the current limitations of state-of-the-art methods for dealing with noisy labels for image classification tasks in the case of web noise distribution. We propose a simple solution to bridge the gap with a fully clean dataset using Dynamic Softening of Out-of-distribution Samples (DSOS), which we design on corrupted versions of the CIFAR-100 dataset, and compare against state-of-the-art algorithms on the web noise perturbated MiniImageNet and Stanford datasets and on real label noise datasets: WebVision 1.0 and Clothing1M. Our work is fully reproducible https://git.io/JKGcj

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 42 of its 52 references that are in the map. First posted on arXiv 26 Oct 2021.

Builds on

42 of 52 references.

References · 42

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30 more
  1. 2020DivideMix: Learning with Noisy Labels as Semi-supervised Learningcited by 691
  2. 2018An Empirical Study of Example Forgetting during Deep Neural Network Learningcited by 669
  3. 2016Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approachcited by 663
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  5. 2020Learning from Noisy Labels with Deep Neural Networks: A Surveycited by 580
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Cited by year

Citing papers per year: 2021: 1; 2022: 3; 2023: 3; 2024: 5; 2025: 2.

14 papers.

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  1. 2021SSR: An Efficient and Robust Framework for Learning with Unknown Label Noisecited by 13
  2. 2022Is your noise correction noisy? PLS: Robustness to label noise with two stage detectioncited by 11
  3. 2023Manifold DivideMix: A Semi-Supervised Contrastive Learning Framework for Severe Label Noisecited by 9
  4. 2022Embedding contrastive unsupervised features to cluster in- and out-of-distribution noise in corrupted image datasetscited by 6
  5. 2022Tripartite: Tackle Noisy Labels by a More Precise Partitioncited by 3
  6. 2023GenKL: An Iterative Framework for Resolving Label Ambiguity and Label Non-conformity in Web Images Via a New Generalized KL Divergencecited by 3
  7. 2024NoisyAG-News: A Benchmark for Addressing Instance-Dependent Noise in Text Classificationcited by 1
  8. 2025Adaptively Point-weighting Curriculum Learningcited by 1
  9. 2023Soft Curriculum for Learning Conditional GANs with Noisy-Labeled and Uncurated Unlabeled Datacited by 0
  10. 2024FLASH: Federated Learning Across Simultaneous Heterogeneitiescited by 0
  11. 2024Data Quality in Edge Machine Learning: A State-of-the-Art Surveycited by 0
  12. 2024An accurate detection is not all you need to combat label noise in web-noisy datasetscited by 0
2 more
  1. 2024Debiased Orthogonal Boundary-Driven Efficient Noise Mitigationcited by 0
  2. 2025Self-Ensemble Post Learning for Noisy Domain Generalizationcited by 0