mlsanityData to 7 Oct 2026

modAL: A modular active learning framework for Python

arXiv 1805.00979 ↗First posted 2 May 2018cs.LG stat.MLcited by 35

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Abstract (arXiv)

modAL is a modular active learning framework for Python, aimed to make active learning research and practice simpler. Its distinguishing features are (i) clear and modular object oriented design (ii) full compatibility with scikit-learn models and workflows. These features make fast prototyping and easy extensibility possible, aiding the development of real-life active learning pipelines and novel algorithms as well. modAL is fully open source, hosted on GitHub at https://github.com/cosmic-cortex/modAL. To assure code quality, extensive unit tests are provided and continuous integration is applied. In addition, a detailed documentation with several tutorials are also available for ease of use. The framework is available in PyPI and distributed under the MIT license.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 May 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 10; 2023: 9; 2024: 2; 2025: 3; 2026: 3.

35 papers.

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  1. 2021Small-Text: Active Learning for Text Classification in Pythoncited by 18
  2. 2021Practical Galaxy Morphology Tools from Deep Supervised Representation Learningcited by 14
  3. 2021Hitting the Target: Stopping Active Learning at the Cost-Based Optimumcited by 9
  4. 2021Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite Imagescited by 9
  5. 2022Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentationcited by 7
  6. 2023Novel Intent Detection and Active Learning Based Classification (Student Abstract)cited by 6
  7. 2022Ground Truth Inference for Weakly Supervised Entity Matchingcited by 5
  8. 2023An Expanded Benchmark that Rediscovers and Affirms the Edge of Uncertainty Sampling for Active Learning in Tabular Datasetscited by 4
  9. 2025General Information Metrics for Improving AI Model Training Efficiencycited by 4
  10. 2022Deep reinforced active learning for multi-class image classificationcited by 3
  11. 2021Seeing and Believing: Evaluating the Trustworthiness of Twitter Userscited by 2
  12. 2021Addressing practical challenges in Active Learning via a hybrid query strategycited by 2
23 more
  1. 2022PyRelationAL: a python library for active learning research and developmentcited by 2
  2. 2022Trust and Believe -- Should We? Evaluating the Trustworthiness of Twitter Userscited by 2
  3. 2023MEGAnno: Exploratory Labeling for NLP in Computational Notebookscited by 2
  4. 2023Implementing Active Learning in Cybersecurity: Detecting Anomalies in Redacted Emailscited by 2
  5. 2021AstronomicAL: An interactive dashboard for visualisation, integration and classification of data using Active Learningcited by 1
  6. 2022Semantic Interactive Learning for Text Classification: A Constructive Approach for Contextual Interactionscited by 1
  7. 2022ALANNO: An Active Learning Annotation System for Mortalscited by 1
  8. 2023Are Good Explainers Secretly Human-in-the-Loop Active Learners?cited by 1
  9. 2025Targeting Misalignment: A Conflict-Aware Framework for Reward-Model-based LLM Alignmentcited by 1
  10. 2021Unsupervised Instance Selection with Low-Label, Supervised Learning for Outlier Detectioncited by 0
  11. 2022SARS-CoV-2 Result Interpretation based on Image Analysis of Lateral Flow Devicescited by 0
  12. 2022Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AIcited by 0
  13. 2022Label-Efficient Interactive Time-Series Anomaly Detectioncited by 0
  14. 2023Decentralized Technologies for AI Hubscited by 0
  15. 2023Combining Thermodynamics-based Model of the Centrifugal Compressors and Active Machine Learning for Enhanced Industrial Design Optimizationcited by 0
  16. 2023Pool-Based Active Learning with Proper Topological Regionscited by 0
  17. 2023ActiveClean: Generating Line-Level Vulnerability Data via Active Learningcited by 0
  18. 2024Frugal Algorithm Selectioncited by 0
  19. 2024regAL: Python Package for Active Learning of Regression Problemscited by 0
  20. 2025IDALC: A Semi-Supervised Framework for Intent Detection and Active Learning based Correctioncited by 0
  21. 2026Active Learning Using Aggregated Acquisition Functions: Accuracy and Sustainability Analysiscited by 0
  22. 2026Active Learning for Biodiversity Monitoring: From Label Efficiency to Reliable Ecological Inferencecited by 0
  23. 2026ALF: An Active Learning Framework for Scientific Discoverycited by 0