🤩 A curated list of awesome Active Learning ! 🤩

Background

(An illustrative example of pool-based active learning. image source: Settles, Burr)

What is Active Learning?

Active learning is a special case of machine learning in which a learning algorithm can interactively query a oracle (or some other information source) to label new data points with the desired outputs.

(The pool-based active learning cycle. image source: Settles, Burr)

There are situations in which unlabeled data is abundant but manual labeling is expensive. In such a scenario, learning algorithms can actively query the oracle for labels. This type of iterative supervised learning is called active learning. Since the learner chooses the examples, the number of examples to learn a concept can often be much lower than the number required in normal supervised learning. With this approach, there is a risk that the algorithm is overwhelmed by uninformative examples. Recent developments are dedicated to multi-label active learning, hybrid active learning and active learning in a single-pass (on-line) context, combining concepts from the field of machine learning (e.g. conflict and ignorance) with adaptive, incremental learning policies in the field of online machine learning.

(source: Wikipedia)

Contributing

If you find the awesome paper/code/book/tutorial or have some suggestions, please feel free to pull requests or contact baifanxxx@gmail.com or chenliangyudavid@gmail.com to add papers using the following Markdown format:

Year | Paper Name | Conference | [Paper](link) | [Code](link) | Tags | Notes |

Tags

Sur.: survey | Cri.: critics | Pool.: pool-based sampling | Str.: stream-based sampling | Syn.: membership query synthesize | Semi.: semi-supervised learning | Self.: self-supervised learning | RL.: reinforcement learning | FS.: few-shot learning | Meta.: meta learning |

Thanks for your valuable contribution to the research community. 😃

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Table of Contents

• Books

• Surveys

• Benchmarks & Evaluation

• Papers

  • 2024
  • 2023
  • 2022
  • 2021
  • 2020
  • 2019
  • 2018
  • 2017
  • Before 2017

• Turtorials

• Tools

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Books

• Chapter 22 Active Learning: A Survey. from Data Classification: Algorithms and Applications Charu C. Aggarwa et al.(CRC Press, 2014)
• Active Learning. Burr Settles. (CMU, 2012)

Surveys

Year	Paper	Author	Publication	Code	Notes
2022	A Comparative Survey of Deep Active Learning	Xueying Zhan et al.	arXiv	code
2021	A Survey on Active Deep Learning: From Model-driven to Data-driven	Peng Liu et al.	CSUR
2020	A Survey of Active Learning for Text Classification using Deep Neural Networks	Christopher Schröder et al.	arXiv
2020	A Survey of Deep Active Learning	Pengzhen Ren et al.	CSUR
2009	Active Learning Literature Survey	Settles, Burr.	University of Wisconsin-Madison Department of Computer Sciences

Benchmarks & Evaluation

• Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessment. Carsten T. Lüth, Till J. Bungert, Lukas Klein, Paul F. Jaeger. (NeurIPS, 2023)
• nnActive: A Framework for Evaluation of Active Learning in 3D Biomedical Segmentation. Carsten T. Lüth, Jonas Traub, Kim-Celine Kahl, Till J. Bungert, Lukas Klein, Lena Krämer, Paul F. Jaeger, Fabian Isensee, Klaus H. Maier-Hein. (TMLR, 2025) [Code] [Results]
• Finally Outshining the Random Baseline: A Simple and Effective Solution for Active Learning in 3D Biomedical Imaging. Carsten T. Lüth, Jonas Traub, Kim-Celine Kahl, Till J. Bungert, Lukas Klein, Lena Krämer, Paul F. Jaeger, Klaus H. Maier-Hein, Fabian Isensee. (TMLR, 2026) [Code] [Results]

Papers

2026

Title	Publication	Paper	Code	Tags	Notes
Finally Outshining the Random Baseline: A Simple and Effective Solution for Active Learning in 3D Biomedical Imaging	TMLR	Paper	Code · Results	Pool.	First AL method consistently outperforming random baseline in 3D biomedical segmentation; practitioner guidelines included

2025

Title	Publication	Paper	Code	Tags	Notes
nnActive: A Framework for Evaluation of Active Learning in 3D Biomedical Segmentation	TMLR	Paper	Code · Results	Pool., Cri.	Largest AL benchmark to date: ~150k GPU hours, 8 methods × 4 datasets × 3 label regimes in 3D biomedical segmentation

2024

Title	Publication	Paper	Code	Tags	Notes
Active Prompt Learning in Vision Language Models	CVPR2024	Paper	Code	Pool., FS.	AL for Vision-Language Model
Active Generalized Category Discovery	CVPR 2024	Paper	Code	Pool.	More generalized AL considering unseen novel categories
Plug and Play Active Learning for Object Detection	CVPR 2024	Paper	Code	Pool.	AL for Object Detection
Entropic Open-Set Active Learning	AAAI 2024	Paper	Code	Pool.	Open-world AL

2023

Title	Publication	Paper	Code	Tags	Notes
Compute-Efficient Active Learning	NeurIPS 2023 Workshop ReALML	Paper	Code	Pool., Syn.	Method-agnostic framework

|Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessment|NeurIPS 2023|Paper|Code| Pool., Cri.| Identifies 5 AL evaluation pitfalls; large-scale benchmark |

2022

Title	Publication	Paper	Code	Tags	Notes
Active Learning Helps Pretrained Models Learn the Intended Task	NeurIPS	paper	code	Pool.
Making Your First Choice: To Address Cold Start Problem in Vision Active Learning	NeurIPS workshop	paper	code	Pool.	Cold-start problem
Active Learning Through a Covering Lens	NeurIPS	paper	code	Pool.
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation	NeurIPS	paper	code	Pool.	Model evaluation
Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning	NeurIPS	paper	code	Pool.
One-Bit Active Query With Contrastive Pairs	CVPR	paper		Pool.	One-bit supervision task
Active label cleaning for improved dataset quality under resource constraints	Nature Communications	paper	code	Pool.	Label cleaning
Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation	CVPR	paper	code	Pool.
Budget-aware Few-shot Learning via Graph Convolutional Network	arXiv	paper		Pool. Meta. FS.
Using Self-Supervised Pretext Tasks for Active Learning	arXiv	paper	code	Pool. SS.	Cold-start problem
Low-Budget Active Learning via Wasserstein Distance: An Integer Programming Approach	ICLR	paper		Pool.	Cold-start problem
Active Learning by Feature Mixing	CVPR	paper	code	Pool.
ALLSH: Active Learning Guided by Local Sensitivity and Hardness	NAACL	paper	code	Semi.	NLP
Coherence-based Label Propagation over Time Series for Accelerated Active Learning	ICLR	paper	code	Pool.	Time series

2021

Title	Publication	Paper	Code	Tags	Notes
Active learning with MaskAL reduces annotation effort for training Mask R-CNN	arXiv	paper	code
MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning	arXiv	paper	code	Pool. Meta. RL.
Can Active Learning Preemptively Mitigate Fairness Issues	ICLR-RAI	paper	code	Pool.	Thinking fairness issues
Sequential Graph Convolutional Network for Active Learning	CVPR	paper	code	Pool.
Task-Aware Variational Adversarial Active Learning	CVPR	paper	code	Pool.
Effective Evaluation of Deep Active Learning on Image Classification Tasks	arXiv	paper		Cri.
Semi-Supervised Active Learning for Semi-Supervised Models: Exploit Adversarial Examples With Graph-Based Virtual Labels	ICCV	paper		Pool. Semi.
Contrastive Coding for Active Learning under Class Distribution Mismatch	ICCV	paper	code	Pool.	Defines a good question
Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering	ACL-IJCNLP	paper	code	Pool.	Thinking about outliers
LADA: Look-Ahead Data Acquisition via Augmentation for Active Learning	NeurIPS	paper		Pool.
Multi-Anchor Active Domain Adaptation for Semantic Segmentation	ICCV	paper	code	Pool.
Active Learning for Lane Detection: A Knowledge Distillation Approach	ICCV	paper		Pool.
Active Contrastive Learning of Audio-Visual Video Representations	ICLR	paper	code	Pool.
Multiple instance active learning for object detection	CVPR	paper	code	Pool.
SEAL: Self-supervised Embodied Active Learning using Exploration and 3D Consistency	NeurIPS	paper		Self.	Robot exploration
Influence Selection for Active Learning	ICCV	paper	code	Pool.
Reducing Label Effort: Self-Supervised meets Active Learning	arXiv	paper		Pool. Self. Cri.	A meaningful attempt on the combination of SS & AL
Towards General and Efficient Active Learning	arXiv	paper	code	Pool. Self.	Single-pass AL based on SS ViT
Cartography Active Learning	EMNLP Findings	paper	code	Pool.
Joint Semi-supervised and Active Learning for Segmentation of Gigapixel Pathology Images with Cost-Effective Labeling	ICCVW	paper		Pool.
PAL : Pretext-based Active Learning	BMVC	paper	code	Pool.	Cold-start problem
Active Learning for Deep Object Detection via Probabilistic Modeling	ICCV	paper	code	Pool.	GMM
Unsupervised Data Selection for Data-Centric Semi-Supervised Learning	arXiv	paper		Pool.	Data selection + SSL
Batch Active Learning at Scale	NeurIPS	paper		Scale. Pool.

2020

Title	Publication	Paper	Code	Tags	Notes
Contextual Diversity for Active Learning	ECCV	paper	code	Pool.
Active Learning for BERT: An Empirical Study	EMNLP	paper	code	Pool.
Reinforced active learning for image segmentation	ICLR	paper	code	Pool. RL.
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds	ICLR	paper	code	Pool.
Adversarial Sampling for Active Learning	WACV	paper		Pool.
Online Active Learning of Reject Option Classifiers	AAAI	paper
ViewAL: Active Learning with Viewpoint Entropy for Semantic Segmentation	CVPR	paper	code	Pool.
Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision	CVPR	paper	code
Deep Reinforcement Active Learning for Medical Image Classification	MICCAI	paper		Pool. RL.
State-Relabeling Adversarial Active Learning	CVPR	paper	code	Pool.
Towards Robust and Reproducible Active Learning Using Neural Networks	arXiv	paper	code	Cri.
Minimax Active Learning	arXiv	paper
Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of Stanene	npj Computational Materials	paper	code
Consistency-Based Semi-supervised Active Learning: Towards Minimizing Labeling Cost	ECCV	paper		Pool. Semi.
Cold-start Active Learning through Self-supervised Language Modeling	EMNLP	paper	code	Pool. SS.

2019

Title	Publication	Paper	Code	Tags	Notes
Generative Adversarial Active Learning for Unsupervised Outlier Detection	TKDE	paper	code
Bayesian Generative Active Deep Learning	ICML	paper	code	Pool. Semi.
Variational Adversarial Active Learning	ICCV	paper	code	Pool. Semi.
Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning	NeurIPS	paper
Active Learning via Membership Query Synthesisfor Semi-supervised Sentence Classification	CoNLL	paper
Discriminative Active Learning	arXiv	paper	code
Semantic Redundancies in Image-Classification Datasets: The 10% You Don’t Need	arXiv	paper
On-the-Fly Bayesian Active Learning of Interpretable Force-Fields for Atomistic Rare Events	npj Computational Materials	paper	code
Bayesian Batch Active Learning as Sparse Subset Approximation	NIPS	paper	code
Learning Loss for Active Learning	CVPR	paper	code	Pool.
Rapid Performance Gain through Active Model Reuse	IJCAI	paper
Parting with Illusions about Deep Active Learning	arXiv	paper		Cri.
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning	NIPS	paper	code

2018

Title	Publication	Paper	Code	Tags	Notes
The Power of Ensembles for Active Learning in Image Classification	CVPR	paper
Adversarial Learning for Semi-Supervised Semantic Segmentation	BMVC	paper	code	Pool. Semi.
A Variance Maximization Criterion for Active Learning	Pattern Recognition	paper	code
Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning	ICLR-WS	paper		Pool. Meta. RL.
Active Learning for Convolutional Neural Networks: A Core-Set Approach	ICLR	paper
Adversarial Active Learning for Sequence Labeling and Generation	IJCAI	paper
Meta-Learning for Batch Mode Active Learning	ICLR-WS	paper
Adversarial Active Learning for Deep Networks: a Margin Based Approach	ICML	paper
CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation	BMVC	paper

2017

Title	Publication	Paper	Code	Tags	Notes
Active Decision Boundary Annotation with Deep Generative Models	ICCV	paper	code
Active One-shot Learning	CoRR	paper	code	Str. RL. FS.
A Meta-Learning Approach to One-Step Active-Learning	AutoML@PKDD/ECML	paper		Pool. Meta.
Generative Adversarial Active Learning	arXiv	paper		Pool. Syn.
Active Learning from Peers	NIPS	paper
Learning Active Learning from Data	NIPS	paper	code	Pool.
Learning Algorithms for Active Learning	ICML	paper
Deep Bayesian Active Learning with Image Data	ICML	paper	code	Pool.
Learning how to Active Learn: A Deep Reinforcement Learning Approach	EMNLP	paper	code	Str. RL.

Before 2017

Year	Title	Publication	Paper	Code	Tags	Notes
2016	Active Image Segmentation Propagation	CVPR	paper
2016	Cost-Effective Active Learning for Deep Image Classification	TCSVT	paper	code
2015	Multi-Label Active Learning from Crowds	arXiv	paper
2015	Active Learning by Learning	AAAI	paper
2014	Beyond Disagreement-based Agnostic Active Learning	NIPS	paper
2014	Active Semi-Supervised Learning Using Sampling Theory for Graph Signals	KDD	paper	code
2013	Active Learning for Probabilistic Hypotheses Usingthe Maximum Gibbs Error Criterion	NIPS	paper
2013	Active Learning for Multi-Objective Optimization	ICML	paper
2012	Batch Active Learning via Coordinated Matching	ICML	paper
2012	Bayesian Optimal Active Search and Surveying	ICML	paper	code
2011	Active Learning Using On-line Algorithms	KDD	paper
2011	Bayesian Active Learning for Classification and Preference Learning	CoRR	paper	code
2011	Active Learning from Crowds	ICML	paper
2011	Ask Me Better Questions: Active Learning Queries Based on Rule Induction	KDD	paper
2010	Active Instance Sampling via Matrix Partition	NIPS	paper
2008	Hierarchical Sampling for Active Learning	ICML	paper
2008	An Analysis of Active Learning Strategies for Sequence Labeling Tasks	EMNLP	paper
2008	Active Learning with Direct Query Construction	KDD	paper
2007	Discriminative Batch Mode Active Learning	NIPS	paper	code
1994	Improving Generalization with Active Learning	Machine Learning	paper

Turtorials

• Overview of Active Learning for Deep Learning. Jacob Gildenblat.
• Active Learning from Theory to Practice. Steve Hanneke, Robert Nowak. (ICML, 2019)

Tools

• Active-Learning-as-a-Service: An Efficient MLOps System for Data-Centric AI. Huang, Yizheng and Zhang, Huaizheng and Li, Yuanming and Lau, Chiew Tong and You, Yang. (2022)
• [BAAL] A Bayesian Active Learning Library. Atighehchian, Parmida and Branchaud-Charron, Frederic and Freyberg, Jan and Pardinas, Rafael and Schell, Lorne. (2019)
• ALiPy: Active Learning in Python. Ying-Peng Tang, Guo-Xiang Li, Sheng-Jun Huang. (NUAA, 2019)
• modAL: A modular active learning framework for Python. Tivadar Danka and Peter Horvath. (2018)