mlsanityData to 7 Oct 2026

Learning from Multiple Annotator Noisy Labels via Sample-wise Label Fusion

arXiv 2207.11327 ↗First posted 22 Jul 2022cs.LGcited by 10

Not on the map. Why?

Abstract (arXiv)

Data lies at the core of modern deep learning. The impressive performance of supervised learning is built upon a base of massive accurately labeled data. However, in some real-world applications, accurate labeling might not be viable; instead, multiple noisy labels (instead of one accurate label) are provided by several annotators for each data sample. Learning a classifier on such a noisy training dataset is a challenging task. Previous approaches usually assume that all data samples share the same set of parameters related to annotator errors, while we demonstrate that label error learning should be both annotator and data sample dependent. Motivated by this observation, we propose a novel learning algorithm. The proposed method displays superiority compared with several state-of-the-art baseline methods on MNIST, CIFAR-100, and ImageNet-100. Our code is available at: https://github.com/zhengqigao/Learning-from-Multiple-Annotator-Noisy-Labels.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 16 of its 26 references that are in the map. First posted on arXiv 22 Jul 2022.

Builds on

16 of 26 references.

References · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  4. 2016Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approachcited by 663
  5. 2017MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labelscited by 652
  6. 2020Learning from Noisy Labels with Deep Neural Networks: A Surveycited by 580
  7. 2017Robust Loss Functions under Label Noise for Deep Neural Networkscited by 441
  8. 2014Training Deep Neural Networks on Noisy Labels with Bootstrappingcited by 322
  9. 2018Joint Optimization Framework for Learning with Noisy Labelscited by 293
  10. 2019Learning From Noisy Labels By Regularized Estimation Of Annotator Confusioncited by 126
  11. 2019Image Classification with Deep Learning in the Presence of Noisy Labels: A Surveycited by 101
  12. 2017Who Said What: Modeling Individual Labelers Improves Classificationcited by 77
4 more
  1. 2019On Symmetric Losses for Learning from Corrupted Labelscited by 53
  2. 2017Learning From Noisy Singly-labeled Datacited by 50
  3. 2021Multimodal Knowledge Expansioncited by 26
  4. 2022Training-Free Robust Multimodal Learning via Sample-Wise Jacobian Regularizationcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 7; 2024: 1; 2025: 1; 2026: 1.

10 papers.

  1. 2023AQuA: A Benchmarking Tool for Label Quality Assessmentcited by 16
  2. 2023Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowdscited by 11
  3. 2025Realistic Evaluation of Deep Partial-Label Learning Algorithmscited by 11
  4. 2026Beyond Consensus: Perspectivist Modeling and Evaluation of Annotator Disagreement in NLPcited by 10
  5. 2023Multi-annotator Deep Learning: A Probabilistic Framework for Classificationcited by 8
  6. 2023Learning to Complement with Multiple Humanscited by 5
  7. 2023Label Smarter, Not Harder: CleverLabel for Faster Annotation of Ambiguous Image Classification with Higher Qualitycited by 3
  8. 2023Handwritten Text Recognition from Crowdsourced Annotationscited by 1
  9. 2023Drawing the Same Bounding Box Twice? Coping Noisy Annotations in Object Detection with Repeated Labelscited by 1
  10. 2024Mixture of Experts based Multi-task Supervise Learning from Crowdscited by 1