mlsanityData to 14 Oct 2026

Active Learning with a Noisy Annotator

arXiv 2504.04506 ↗First posted 6 Apr 2025cs.LG cs.CVcited by 3

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Abstract (arXiv)

Active Learning (AL) aims to reduce annotation costs by strategically selecting the most informative samples for labeling. However, most active learning methods struggle in the low-budget regime where only a few labeled examples are available. This issue becomes even more pronounced when annotators provide noisy labels. A common AL approach for the low- and mid-budget regimes focuses on maximizing the coverage of the labeled set across the entire dataset. We propose a novel framework called Noise-Aware Active Sampling (NAS) that extends existing greedy, coverage-based active learning strategies to handle noisy annotations. NAS identifies regions that remain uncovered due to the selection of noisy representatives and enables resampling from these areas. We introduce a simple yet effective noise filtering approach suitable for the low-budget regime, which leverages the inner mechanism of NAS and can be applied for noise filtering before model training. On multiple computer vision benchmarks, including CIFAR100 and ImageNet subsets, NAS significantly improves performance for standard active learning methods across different noise types and rates.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 33 of its 52 references that are in the map. First posted on arXiv 6 Apr 2025.

Builds on

33 of 52 references.

References · 33

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  2. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  3. 2020Improved Baselines with Momentum Contrastive Learningcited by 2,287
  4. 2018Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labelscited by 1,059
  5. 2017A Closer Look at Memorization in Deep Networkscited by 942
  6. 2020DivideMix: Learning with Noisy Labels as Semi-supervised Learningcited by 692
  7. 2016Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approachcited by 665
  8. 2019Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Boundscited by 610
  9. 2020Learning from Noisy Labels with Deep Neural Networks: A Surveycited by 582
  10. 2020SCAN: Learning to Classify Images without Labelscited by 352
  11. 2018Joint Optimization Framework for Learning with Noisy Labelscited by 293
  12. 2020Identifying Mislabeled Data using the Area Under the Margin Rankingcited by 261
21 more
  1. 2021Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotationscited by 256
  2. 2020Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learningcited by 142
  3. 2022Active Learning on a Budget: Opposite Strategies Suit High and Low Budgetscited by 133
  4. 2015Webly Supervised Learning of Convolutional Networkscited by 95
  5. 2021FINE Samples for Learning with Noisy Labelscited by 82
  6. 2022UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learningcited by 77
  7. 2020A Topological Filter for Learning with Label Noisecited by 76
  8. 2022Active Learning Through a Covering Lenscited by 66
  9. 2020Towards Robust and Reproducible Active Learning Using Neural Networkscited by 54
  10. 2022ProMix: Combating Label Noise via Maximizing Clean Sample Utilitycited by 48
  11. 2015Active Learning from Weak and Strong Labelerscited by 22
  12. 2023LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learningcited by 20
  13. 2023How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budgetcited by 17
  14. 2024Generalized Coverage for More Robust Low-Budget Active Learningcited by 16
  15. 2015Active Distance-Based Clustering using K-medoidscited by 8
  16. 2021Sample Prior Guided Robust Model Learning to Suppress Noisy Labelscited by 8
  17. 2023Improved Algorithm for Deep Active Learning under Imbalance via Optimal Separationcited by 8
  18. 2022Improved Adaptive Algorithm for Scalable Active Learning with Weak Labelercited by 4
  19. 2022The Dynamic of Consensus in Deep Networks and the Identification of Noisy Labelscited by 3
  20. 2023Deep Active Learning with Noisy Oracle in Object Detectioncited by 3
  21. 2023Robust Assignment of Labels for Active Learning with Sparse and Noisy Annotationscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Robust Self-Training with Closed-loop Label Correction for Learning from Noisy Labelscited by 0
  2. 2026An Analysis of Active Learning Algorithms using Real-World Crowd-sourced Text Annotationscited by 0
  3. 2026Hard Cases, Bad Labels: Testing Error Exposure and Error Location in Uncertainty Sampling Under Bounded Label Noisecited by 0