# Diverse mini-batch Active Learning

arXiv: https://arxiv.org/abs/1901.05954 · HTML: https://mlsanity.com/paper/1901.05954/

## Key facts

- Cited by 89 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2019.

## Built on by (most cited first, 30 of 89)

- [Active Learning by Acquiring Contrastive Examples](https://mlsanity.com/paper/2109.03764/) (2021)
- [Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets](https://mlsanity.com/paper/2202.02794/) (2022)
- [Batch Active Learning at Scale](https://mlsanity.com/paper/2107.14263/) (2021)
- [A Comparative Survey of Deep Active Learning](https://mlsanity.com/paper/2203.13450/) (2022)
- [Active Learning Through a Covering Lens](https://mlsanity.com/paper/2205.11320/) (2022)
- [Data Curation Alone Can Stabilize In-context Learning](https://mlsanity.com/paper/2212.10378/) (2022)
- [Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach](https://mlsanity.com/paper/2405.15613/) (2024)
- [A Framework and Benchmark for Deep Batch Active Learning for Regression](https://mlsanity.com/paper/2203.09410/) (2022)
- [A Survey of Active Learning for Natural Language Processing](https://mlsanity.com/paper/2210.10109/) (2022)
- [Deep Active Learning by Leveraging Training Dynamics](https://mlsanity.com/paper/2110.08611/) (2021)
- [Plug and Play Active Learning for Object Detection](https://mlsanity.com/paper/2211.11612/) (2022)
- [Data Shapley Valuation for Efficient Batch Active Learning](https://mlsanity.com/paper/2104.08312/) (2021)
- [Cartography Active Learning](https://mlsanity.com/paper/2109.04282/) (2021)
- [The Efficiency Spectrum of Large Language Models: An Algorithmic Survey](https://mlsanity.com/paper/2312.00678/) (2023)
- [A Lagrangian Duality Approach to Active Learning](https://mlsanity.com/paper/2202.04108/) (2022)
- [Neural Active Learning with Performance Guarantees](https://mlsanity.com/paper/2106.03243/) (2021)
- [Diversity Enhanced Active Learning with Strictly Proper Scoring Rules](https://mlsanity.com/paper/2110.14171/) (2021)
- [MEAL: Stable and Active Learning for Few-Shot Prompting](https://mlsanity.com/paper/2211.08358/) (2022)
- [Discrepancy-Based Active Learning for Domain Adaptation](https://mlsanity.com/paper/2103.03757/) (2021)
- [Active Learning Strategies for Weakly-supervised Object Detection](https://mlsanity.com/paper/2207.12112/) (2022)
- [On Data Efficiency of Meta-learning](https://mlsanity.com/paper/2102.00127/) (2021)
- [Fast Rates in Pool-Based Batch Active Learning](https://mlsanity.com/paper/2202.05448/) (2022)
- [Uncertainty Herding: One Active Learning Method for All Label Budgets](https://mlsanity.com/paper/2412.20644/) (2024)
- [D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Bias](https://mlsanity.com/paper/2305.17013/) (2023)
- [Multi-class Text Classification using BERT-based Active Learning](https://mlsanity.com/paper/2104.14289/) (2021)
- [Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regression](https://mlsanity.com/paper/2211.10360/) (2022)
- [SALAD: Source-free Active Label-Agnostic Domain Adaptation for Classification, Segmentation and Detection](https://mlsanity.com/paper/2205.12840/) (2022)
- [Best Practices in Active Learning for Semantic Segmentation](https://mlsanity.com/paper/2302.04075/) (2023)
- [Neural Active Learning Beyond Bandits](https://mlsanity.com/paper/2404.12522/) (2024)
- [Deep Indexed Active Learning for Matching Heterogeneous Entity Representations](https://mlsanity.com/paper/2104.03986/) (2021)

## Abstract (arXiv)

We study the problem of reducing the amount of labeled training data required to train supervised classification models. We approach it by leveraging Active Learning, through sequential selection of examples which benefit the model most. Selecting examples one by one is not practical for the amount of training examples required by the modern Deep Learning models. We consider the mini-batch Active Learning setting, where several examples are selected at once. We present an approach which takes into account both informativeness of the examples for the model, as well as the diversity of the examples in a mini-batch. By using the well studied K-means clustering algorithm, this approach scales better than the previously proposed approaches, and achieves comparable or better performance.