# Sample Noise Impact on Active Learning

arXiv: https://arxiv.org/abs/2109.01372 · HTML: https://mlsanity.com/paper/2109.01372/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 8 references that are in the map.
- First posted on arXiv 3 Sep 2021.

## Builds on (most cited first, 6 of 6)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https://mlsanity.com/paper/1906.08158/) (2019)
- [Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks](https://mlsanity.com/paper/2103.14749/) (2021)
- [Diverse mini-batch Active Learning](https://mlsanity.com/paper/1901.05954/) (2019)
- [Exploring Representativeness and Informativeness for Active Learning](https://mlsanity.com/paper/1904.06685/) (2019)
- [Rebuilding Trust in Active Learning with Actionable Metrics](https://mlsanity.com/paper/2012.11365/) (2020)

## Built on by (most cited first, 4 of 4)

- [Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event Detection](https://mlsanity.com/paper/2505.20956/) (2025)
- [OpenAL: Evaluation and Interpretation of Active Learning Strategies](https://mlsanity.com/paper/2304.05246/) (2023)
- [Survey of Active Learning Hyperparameters: Insights from a Large-Scale Experimental Grid](https://mlsanity.com/paper/2506.03817/) (2025)
- [CAAL: Confidence-Aware Active Learning for Heteroscedastic Atmospheric Regression](https://mlsanity.com/paper/2602.11825/) (2026)

## 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.