# ALdataset: a benchmark for pool-based active learning

arXiv: https://arxiv.org/abs/2010.08161 · HTML: https://mlsanity.com/paper/2010.08161/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes](https://mlsanity.com/paper/2309.05477/) (2023)
- [Active Learning Methods for Efficient Data Utilization and Model Performance Enhancement](https://mlsanity.com/paper/2504.16136/) (2025)

## Abstract (arXiv)

Active learning (AL) is a subfield of machine learning (ML) in which a learning algorithm could achieve good accuracy with less training samples by interactively querying a user/oracle to label new data points. Pool-based AL is well-motivated in many ML tasks, where unlabeled data is abundant, but their labels are hard to obtain. Although many pool-based AL methods have been developed, the lack of a comparative benchmarking and integration of techniques makes it difficult to: 1) determine the current state-of-the-art technique; 2) evaluate the relative benefit of new methods for various properties of the dataset; 3) understand what specific problems merit greater attention; and 4) measure the progress of the field over time. To conduct easier comparative evaluation among AL methods, we present a benchmark task for pool-based active learning, which consists of benchmarking datasets and quantitative metrics that summarize overall performance. We present experiment results for various active learning strategies, both recently proposed and classic highly-cited methods, and draw insights from the results.