# Bayesian active learning for production, a systematic study and a reusable library

arXiv: https://arxiv.org/abs/2006.09916 · HTML: https://mlsanity.com/paper/2006.09916/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2020.

## Built on by (most cited first, 15 of 15)

- [Prediction-Oriented Bayesian Active Learning](https://mlsanity.com/paper/2304.08151/) (2023)
- [Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning](https://mlsanity.com/paper/2106.12059/) (2021)
- [Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessment](https://mlsanity.com/paper/2301.10625/) (2023)
- [Small-Text: Active Learning for Text Classification in Python](https://mlsanity.com/paper/2107.10314/) (2021)
- [Can Active Learning Preemptively Mitigate Fairness Issues?](https://mlsanity.com/paper/2104.06879/) (2021)
- [Making Better Use of Unlabelled Data in Bayesian Active Learning](https://mlsanity.com/paper/2404.17249/) (2024)
- [Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions](https://mlsanity.com/paper/2401.04305/) (2024)
- [Efficacy of Bayesian Neural Networks in Active Learning](https://mlsanity.com/paper/2104.00896/) (2021)
- [LiDAR dataset distillation within bayesian active learning framework: Understanding the effect of data augmentation](https://mlsanity.com/paper/2202.02661/) (2022)
- [PyRelationAL: a python library for active learning research and development](https://mlsanity.com/paper/2205.11117/) (2022)
- [Active Learning to Guide Labeling Efforts for Question Difficulty Estimation](https://mlsanity.com/paper/2409.09258/) (2024)
- [ALScope: A Unified Toolkit for Deep Active Learning](https://mlsanity.com/paper/2508.04937/) (2025)
- [Domain Adaptation from Scratch](https://mlsanity.com/paper/2209.00830/) (2022)
- [Evaluating the effect of data augmentation and BALD heuristics on distillation of Semantic-KITTI dataset](https://mlsanity.com/paper/2302.10679/) (2023)
- [Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active Learning Pipeline for Speech Recognition](https://mlsanity.com/paper/2406.02566/) (2024)

## Abstract (arXiv)

Active learning is able to reduce the amount of labelling effort by using a machine learning model to query the user for specific inputs. While there are many papers on new active learning techniques, these techniques rarely satisfy the constraints of a real-world project. In this paper, we analyse the main drawbacks of current active learning techniques and we present approaches to alleviate them. We do a systematic study on the effects of the most common issues of real-world datasets on the deep active learning process: model convergence, annotation error, and dataset imbalance. We derive two techniques that can speed up the active learning loop such as partial uncertainty sampling and larger query size. Finally, we present our open-source Bayesian active learning library, BaaL.