mlsanityData to 7 Oct 2026

Sample Noise Impact on Active Learning

arXiv 2109.01372 ↗First posted 3 Sep 2021stat.ML cs.LGcited by 4

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Abstract (arXiv)

This work explores the effect of noisy sample selection in active learning strategies. We show on both synthetic problems and real-life use-cases that knowledge of the sample noise can significantly improve the performance of active learning strategies. Building on prior work, we propose a robust sampler, Incremental Weighted K-Means that brings significant improvement on the synthetic tasks but only a marginal uplift on real-life ones. We hope that the questions raised in this paper are of interest to the community and could open new paths for active learning research.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 6 of its 8 references that are in the map. First posted on arXiv 3 Sep 2021.

Builds on

6 of 8 references.

References · 6

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  2. 2019BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learningcited by 428
  3. 2021Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarkscited by 388
  4. 2019Diverse mini-batch Active Learningcited by 88
  5. 2019Exploring Representativeness and Informativeness for Active Learningcited by 21
  6. 2020Rebuilding Trust in Active Learning with Actionable Metricscited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 1.

4 papers.

  1. 2025Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event Detectioncited by 2
  2. 2023OpenAL: Evaluation and Interpretation of Active Learning Strategiescited by 1
  3. 2025Survey of Active Learning Hyperparameters: Insights from a Large-Scale Experimental Gridcited by 0
  4. 2026CAAL: Confidence-Aware Active Learning for Heteroscedastic Atmospheric Regressioncited by 0