qiaoxingli / awesome-active-learning

A curated list of awesome Active Learning

Awesome Active Learning

🤩 A curated list of awesome Active Learning ! 🤩

Background

(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.

(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 [email protected] or [email protected] to add papers using the following Markdown format:

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

Thanks for your valuable contribution to the research community. 😃

Table of Contents

• Books
• Surveys
• Papers
• Turtorials
• Tools

Books

• Active Learning. Burr Settles. (CMU, 2012)

Surveys

• Active Learning Literature Survey. Settles, Burr. (2009)
• A Survey of Deep Active Learning. Pengzhen Ren et al. (2020)
• From Model-driven to Data-driven: A Survey on Active Deep Learning. Peng Liu et al. (2021)

Papers

Tags

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

Before 2017

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

2017

Title	Conf	Paper	Code	Tags	Notes
Active Decision Boundary Annotation with Deep Generative Models	ICCV	paper
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.

2018

Title	Conf	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
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

2019

Title	Conf	Paper	Code	Tags	Notes
ViewAL: Active Learning with Viewpoint Entropy for Semantic Segmentation	CVPR	paper		Pool.
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
Semantic Redundancies in Image-Classification Datasets: The 10% You Don’t Need	arXiv	paper
Bayesian Batch Active Learning as Sparse Subset Approximation	NIPS	paper
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

2020

Title	Conf	Paper	Code	Tags	Notes
Reinforced active learning for image segmentation	ICLR	paper	code	Pool. RL.
[BADGE] Batch Active learning by Diverse Gradient Embeddings	ICLR	paper	code	Pool.
Adversarial Sampling for Active Learning	WACV	paper		Pool.
Online Active Learning of Reject Option Classifiers	AAAI	paper
Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision	CVPR	paper
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		Cri.
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		Pool. SS.

2021

Title	Conf	Paper	Code	Tags	Notes
MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning	arXiv	paper		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.	Cold-start problem

2022

Title	Conf	Paper	Code	Tags	Notes
Budget-aware Few-shot Learning via Graph Convolutional Network	arXiv	paper		Pool. Meta. FS.

Turtorials

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

Tools

• ALiPy: Active Learning in Python. Ying-Peng Tang, Guo-Xiang Li, Sheng-Jun Huang. (NUAA, 2019)
• [BAAL] A Bayesian Active Learning Library. Atighehchian, Parmida and Branchaud-Charron, Frederic and Freyberg, Jan and Pardinas, Rafael and Schell, Lorne. (2019)