# Discriminative Active Learning

arXiv: https://arxiv.org/abs/1907.06347 · HTML: https://mlsanity.com/paper/1907.06347/

## Key facts

- Cited by 98 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2019.

## Built on by (most cited first, 30 of 98)

- [Fine-Tuning Language Models from Human Preferences](https://mlsanity.com/paper/1909.08593/) (2019)
- [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)
- [A Comparative Survey of Deep Active Learning](https://mlsanity.com/paper/2203.13450/) (2022)
- [Gone Fishing: Neural Active Learning with Fisher Embeddings](https://mlsanity.com/paper/2106.09675/) (2021)
- [Efficient Methods for Natural Language Processing: A Survey](https://mlsanity.com/paper/2209.00099/) (2022)
- [Active Learning Through a Covering Lens](https://mlsanity.com/paper/2205.11320/) (2022)
- [Active Learning for Domain Adaptation: An Energy-Based Approach](https://mlsanity.com/paper/2112.01406/) (2021)
- [A Survey of Active Learning for Natural Language Processing](https://mlsanity.com/paper/2210.10109/) (2022)
- [Uncertainty Estimation for Language Reward Models](https://mlsanity.com/paper/2203.07472/) (2022)
- [A comprehensive survey on deep active learning in medical image analysis](https://mlsanity.com/paper/2310.14230/) (2023)
- [GALAXY: Graph-based Active Learning at the Extreme](https://mlsanity.com/paper/2202.01402/) (2022)
- [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)
- [ALLSH: Active Learning Guided by Local Sensitivity and Hardness](https://mlsanity.com/paper/2205.04980/) (2022)
- [Multi-Resolution Active Learning of Fourier Neural Operators](https://mlsanity.com/paper/2309.16971/) (2023)
- [Batch Multi-Fidelity Active Learning with Budget Constraints](https://mlsanity.com/paper/2210.12704/) (2022)
- [Streaming Active Learning with Deep Neural Networks](https://mlsanity.com/paper/2303.02535/) (2023)
- [Small-Text: Active Learning for Text Classification in Python](https://mlsanity.com/paper/2107.10314/) (2021)
- [Algorithm Selection for Deep Active Learning with Imbalanced Datasets](https://mlsanity.com/paper/2302.07317/) (2023)
- [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)
- [How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budget](https://mlsanity.com/paper/2306.03543/) (2023)
- [ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation](https://mlsanity.com/paper/2310.14979/) (2023)
- [Goldilocks: Just-Right Tuning of BERT for Technology-Assisted Review](https://mlsanity.com/paper/2105.01044/) (2021)
- [Multi-class Text Classification using BERT-based Active Learning](https://mlsanity.com/paper/2104.14289/) (2021)
- [Active Learning Over Multiple Domains in Natural Language Tasks](https://mlsanity.com/paper/2202.00254/) (2022)
- [TiDAL: Learning Training Dynamics for Active Learning](https://mlsanity.com/paper/2210.06788/) (2022)
- [Gaussian Switch Sampling: A Second Order Approach to Active Learning](https://mlsanity.com/paper/2302.12018/) (2023)

## Abstract (arXiv)

We propose a new batch mode active learning algorithm designed for neural networks and large query batch sizes. The method, Discriminative Active Learning (DAL), poses active learning as a binary classification task, attempting to choose examples to label in such a way as to make the labeled set and the unlabeled pool indistinguishable. Experimenting on image classification tasks, we empirically show our method to be on par with state of the art methods in medium and large query batch sizes, while being simple to implement and also extend to other domains besides classification tasks. Our experiments also show that none of the state of the art methods of today are clearly better than uncertainty sampling when the batch size is relatively large, negating some of the reported results in the recent literature.