# DeepAL: Deep Active Learning in Python

arXiv: https://arxiv.org/abs/2111.15258 · HTML: https://mlsanity.com/paper/2111.15258/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Adversarial Active Learning for Deep Networks: a Margin Based Approach](https://mlsanity.com/paper/1802.09841/) (2018)
- [libact: Pool-based Active Learning in Python](https://mlsanity.com/paper/1710.00379/) (2017)

## Built on by (most cited first, 13 of 13)

- [A Comparative Survey of Deep Active Learning](https://mlsanity.com/paper/2203.13450/) (2022)
- [Marich: A Query-efficient Distributionally Equivalent Model Extraction Attack using Public Data](https://mlsanity.com/paper/2302.08466/) (2023)
- [Learn, Unlearn and Relearn: An Online Learning Paradigm for Deep Neural Networks](https://mlsanity.com/paper/2303.10455/) (2023)
- [Active Transfer Learning for Efficient Video-Specific Human Pose Estimation](https://mlsanity.com/paper/2311.05041/) (2023)
- [Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios](https://mlsanity.com/paper/2207.01190/) (2022)
- [Disambiguation of Company names via Deep Recurrent Networks](https://mlsanity.com/paper/2303.05391/) (2023)
- [ALE: A Simulation-Based Active Learning Evaluation Framework for the Parameter-Driven Comparison of Query Strategies for NLP](https://mlsanity.com/paper/2308.02537/) (2023)
- [Direct Acquisition Optimization for Low-Budget Active Learning](https://mlsanity.com/paper/2402.06045/) (2024)
- [Probing Perfection: The Relentless Art of Meddling for Pulmonary Airway Segmentation from HRCT via a Human-AI Collaboration Based Active Learning Method](https://mlsanity.com/paper/2407.03542/) (2024)
- [Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AI](https://mlsanity.com/paper/2207.09109/) (2022)
- [Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data](https://mlsanity.com/paper/2412.07520/) (2024)
- [ATGen: A Framework for Active Text Generation](https://mlsanity.com/paper/2506.23342/) (2025)
- [Active Learning for Biodiversity Monitoring: From Label Efficiency to Reliable Ecological Inference](https://mlsanity.com/paper/2609.27409/) (2026)

## Abstract (arXiv)

We present DeepAL, a Python library that implements several common strategies for active learning, with a particular emphasis on deep active learning. DeepAL provides a simple and unified framework based on PyTorch that allows users to easily load custom datasets, build custom data handlers, and design custom strategies without much modification of codes. DeepAL is open-source on Github and welcome any contribution.