mlsanityData to 7 Oct 2026

Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension

arXiv 2405.03386 ↗First posted 6 May 2024cs.LGcited by 3

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Abstract (arXiv)

Training with noisy class labels impairs neural networks' generalization performance. In this context, mixup is a popular regularization technique to improve training robustness by making memorizing false class labels more difficult. However, mixup neglects that multiple annotators, e.g., crowdworkers, typically provide class labels. Therefore, we propose an extension of mixup, which handles multiple class labels per instance while considering which class label originates from which annotator. Integrated into our multi-annotator classification framework annot-mix, it performs superiorly to eleven (mostly state-of-the-art) approaches in an evaluation study with eleven datasets comprising noisy class labels from either human or simulated annotators. Our code is publicly available through our GitHub repository at https://github.com/ies-research/multi-annotator-machine-learning/tree/annot-mix

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 25 of its 55 references that are in the map. First posted on arXiv 6 May 2024.

Builds on

25 of 55 references.

References · 25

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,764
  4. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  5. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  6. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,875
  7. 2015Character-level Convolutional Networks for Text Classificationcited by 2,293
  8. 2019On the Variance of the Adaptive Learning Rate and Beyondcited by 763
  9. 2020Learning from Noisy Labels with Deep Neural Networks: A Surveycited by 580
  10. 2021Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotationscited by 253
  11. 2019Human uncertainty makes classification more robustcited by 231
  12. 2019Learning From Noisy Labels By Regularized Estimation Of Annotator Confusioncited by 126
13 more
  1. 2017Deep learning from crowdscited by 105
  2. 2019Image Classification with Deep Learning in the Presence of Noisy Labels: A Surveycited by 101
  3. 2017Learning From Noisy Singly-labeled Datacited by 50
  4. 2021End-to-End Weak Supervisioncited by 33
  5. 2020Learning from Crowds by Modeling Common Confusionscited by 32
  6. 2021A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classificationcited by 17
  7. 2018Learning Supervised Topic Models for Classification and Regression from Crowdscited by 16
  8. 2021An Instance-Dependent Simulation Framework for Learning with Label Noisecited by 14
  9. 2023Deep Learning From Crowdsourced Labels: Coupled Cross-entropy Minimization, Identifiability, and Regularizationcited by 12
  10. 2018Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexacited by 11
  11. 2022Identifying Chinese Opinion Expressions with Extremely-Noisy Crowdsourcing Annotationscited by 9
  12. 2023Coupled Confusion Correction: Learning from Crowds with Sparse Annotationscited by 9
  13. 2023Multi-annotator Deep Learning: A Probabilistic Framework for Classificationcited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024dopanim: A Dataset of Doppelganger Animals with Noisy Annotations from Multiple Humanscited by 3
  2. 2025crowd-hpo: Realistic Hyperparameter Optimization and Benchmarking for Learning from Crowds with Noisy Labelscited by 0
  3. 2026DisMix: Order-Aware Mixup for Medical Imaging via Disentangling Ordinal and Non-Ordinal Featurescited by 0