AL Applications

In this chapter, we will divide the works into two sections: scientific applications and industrial applications.

(Note that the works list in this pages are works I browsed in background reading. There might be more important works in the corresponding fields not listed here. Besides, I can't ensure the works are representative in the fields. So if you have any comments and recommendations, pls let me know.)

AL Applications
Scientific Applications (alphabetical order)
Industrial Applications (alphabetical order)

Scientific Applications (alphabetical order)

The scientific applications of AL are about biology, chemistry, physics or things about experiment design and analysis.

Astronomy

Spectroscopic Surveys

Active deep learning method for the discovery of objects of interest in large spectroscopic surveys: We apply active learning classification methods supported by deep convolutional neural networks to automatically identify complex emission-line shapes in multi-million spectra archives.
Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients [2020]: The Recommendation System for Spectroscopic follow- up (RESSPECT) project aims to enable the construction of optimized training samples for the Rubin Observatory Legacy Survey of Space and Time (LSST), taking into account a realistic description of the astronomical data environment.

Reduce Simulation Costs

Active Learning for Computationally Efficient Distribution of Binary Evolution Simulations [2022]

Biology

Structural Biology:

Active machine learning for transmembrane helix prediction [2010, BMC Bioinform]
Investigating Active Learning and Meta-Learning for Iterative Peptide Design [2020, JCM]
Large scale active-learning-guided exploration for in vitro protein production optimization [2020, Nature Communications]
Active learning to classify macromolecular structures in situ for less supervision in cryo-electron tomography [Bioinformatics]

Cell Classification:

Active feature selection discovers minimal gene-sets for classifying cell-types and disease states in single-cell mRNA-seq data [2021]

Molecular Property Prediction：

ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction [2020, KDD]
Tyger: Task-Type-Generic Active Learning for Molecular Property Prediction [2022]

Others:

Active learning for efficient analysis of high-throughput nanopore data [2022, Bioinformatics]

Chemistry

Chemical natural language processing

Active Learning Yields Better Training Data for Scientific Named Entity Recognition [2020]

Experiment Design/Experimental Condition Selection

Active machine learning-driven experimentation to determine compound effects on protein patterns [2016, Elife]
Categorical Matrix Completion with Active Learning for High-throughput Screening [2020, IEEE Transactions on Computational Biology and Bioinformatics]: Categorical matrix completion is designed to accurately impute the missing experiments while margin sampling is also implemented for uncertainty estimation.
Active Learning Approach to Optimization of Experimental Control [2020, CHIN. PHYS. LETT.]: Also an optimization problem, where they try to get the optimal control parameters.
AI-Assisted Scientific Data Collection with Iterative Human Feedback [2021]
Active Learning for the Optimal Design of Multinomial Classification in Physics [2021]
Active learning applied to automated physical systems increases the rate of discovery [2023, scientific reports]

Geology

Lithology Identification:

Active Domain Adaptation With Application to Intelligent Logging Lithology Identification [IEEE TCYB]
Evaluation of active learning algorithms for formation lithology identification [2021, Journal of Petroleum Science and Engineering]

Materials

Materials Design and Discovery

Bias free multi-objective active learning for materials design and discovery [2020]
Active learning for the power factor prediction in diamond-like thermoelectric materials [2020, npj Computational Materials]
Active Discovery of Catalysts for Sustainable Energy Storage [2021]
Active discovery of organic semiconductors [2021, Nature communications]
Deep active learning for constitutive modelling of granular materials: From representative volume elements to implicit finite element modelling [2023, International Journal of Plasticity]
Active Learning Platform for Accelerating the Search for High-Voltage Cathode Materials in an Extensive Chemical Space [2023, J. Phys. Chem. C]
Autonomous experiments using active learning and AI [2023, Nature Reviews Materials]

Math and Statistics

Model checking:

Active and sparse methods in smoothed model checking

Non-Parametric Estimation:

Improved Bounds for Agnostic Active Learning of Single Index Models [2023, NeurIPS Workshop ReALML]

Physics

Condensed Matter Physics

Molecular Dynamics:

[Active learning accelerates ab initio molecular dynamics on reactive energy surfaces [2020, Chem]
Active Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures [2021, Advanced Intelligent Systems]
Nanohardness from First Principles with Active Learning on Atomic Environments [2022, JCTC]
Batch active learning for accelerating the development of interatomic potentials [2022, Comput. Mater. Sci.]

Transition State Calculation:

Active Learning for Transition State Calculation

Material Property Discovery:

Active learning-assisted neutron spectroscopy with log-Gaussian processes [2023, nature communications]

Quantum Physics

Active Learning of Quantum System Hamiltonians yields Query Advantage [2021]

Scientific Computing

Deep Active Learning for Scientific Computing in the Wild [2023]

Industrial Applications (alphabetical order)

The industrial applications of AL are about the practical problems or specific requirements in the industry.

Answer Selection

Combination of Active Learning and Self-Paced Learning for Deep Answer Selection with Bayesian Neural Network [2020, ECAI]

Accusation

Deep Learning-Powered Iterative Combinatorial Auctions with Active Learning [2023, AAMAS]

Autonomous Driving

Survey Paper:

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey [2022]

Line detection:

Active Learning for Lane Detection: A Knowledge Distillation Approach [2021, ICCV]: Evaluate the uncertainty by the teacher-student models.

Others:

Fleet Active Learning: A Submodular Maximization Approach [2023, CoRL]

Brain Mapping in a High Performance Computing Environment

About neuron segmentation and tracing at scale.

Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment [2020, Arxiv]

Communication

Eg. 5g, Traffic Classification

Active Popularity Learning with Cache Hit Ratio Guarantees using a Matrix Completion Committee [2020]: The object is modeling the optimal cache strategy in 5g wireless communication. AL is to learn the content popularities (criterion for determining whether cached or not) since it allows the system to leverage the trade-off between exploration (caching new files) and exploitation (use known files to cache). Specifically, AL is used to do matrix completion on the missing entries of the demand matrix.
Active content popularity learning and caching optimization with hit ratio guarantees [2020, IEEE Access]: Similar to the last one but with more details. A query is defined as the response received from the user terminal to the system.
Active Learning for Network Traffic Classification: A Technical Survey [2021, Arxiv]
Active Sensing for Communications by Learning [2021]
Learning from the Best: Active Learning for Wireless Communications [2024, IEEE Wirel. Commun.]

Crowd Counting

Active Crowd Counting with Limited Supervision [2020, Arxiv]: An multi-domain crowd counting framework. Use discriminator to learn a feature extractor for the crowd density regression for all the domains.
Uncertainty Estimation and Sample Selection for Crowd Counting [2020, Arxiv]

Dataset Engineering

Dataset Building/ Data Annotation

Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop [2015, Arxiv]
A Simple Yet Brisk And Efficient Active Learning Platform For Text Classification
Paladin: an annotation tool based on active and proactive learning [2021]
Scale AI
Appen (Figure-Eight)
ClickWorker
PEANUT: A Human-AI Collaborative Tool for Annotating Audio-Visual Data [2023, UIST]

Data Enrichment

ActiveDeeper: A Model-based Active Data Enrichment System: Use keywords provide by the oracles to search in a database to build a dataset for the current task.

Design for Crowdworkers

Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants [2021]

Others

A Pre-trained Data Deduplication Model based on Active Learning [2023, IEEE Trans. Hum.-Mach. Syst]

Dialog Policy Learning (intelligent system)

Dialog Policy Learning for Joint Clarification and Active Learning Queries [2020, Arxiv]

Delivery System

Customer address resolution:

Learning Geolocation by Accurately Matching Customer Addresses via Graph based Active Learning [2023, WWW]

Disguised Faces Recognition

A-LINK: Recognizing Disguised Faces via Active Learning based Inter-Domain Knowledge [2019]
A2-LINK: Recognizing Disguised Faces via Active Learning and Adversarial Noise Based Inter-Domain Knowledge [2020, IEEE trans. biom.]

Drug Discovery

Active-learning strategies in computer-assisted drug discovery. [2015, Drug Discov.]
Active learning for computational chemogenomics [2017, Future Medicinal Chemistry]
Evaluation of Categorical Matrix Completion Algorithms: Towards Improved Active Learning for Drug Discovery [2021, System Biology]
Active Learning for Drug Design: A Case Study on the Plasma Exposure of Orally Administered Drugs [2021, J MED CHEM]
A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening [2024, Cancers]

Ecology

Automated wildlife image classification: An active learning tool for ecological applications [2023]

Electric Distribution Analysis

An Active Learning-based Approach for Hosting Capacity Analysis in Distribution Systems [2023]

Fault Diagnosis of Machinery

Fault Diagnosis of Rotating Machinery With Limited Expert Interaction: A Multicriteria Active Learning Approach Based on Broad Learning System [2022, ]

Gas Reservoir Prediction

Reservoir prediction through cost⁃sensitive active learning [2020, JOURNAL OF NANJING UNIVERSITY]

Human Computer Interaction (HCI)

Generalisable Dialogue-based Approach for Active Learning of Activities of Daily Living [2023, ACM Trans. Interact. Intell. Syst.]
FedAWR : An Interactive Federated Active Learning Framework for Air Writing Recognition [2023, IEEE Trans. Mob. Comput.]

Internet of Things

DeepWE: A Deep Bayesian Active Learning Waypoint Estimator for Indoor walkers [2023, IEEE Internet of Things Journal]

Labeling System

This is the most direct industrial application.

Firms:

Scale
Figure8 (It seems to be closed.)

Papers:

AI-Assisted Human Labeling: Batching for Efficiency without Overreliance [2021, ACM-HCI]

Malware Detection

Q-learning and LSTM based deep active learning strategy for malware defense in industrial IoT applications [2021, Multimedia Tools and Applications]

Medical Research

Public Health

Active Learning to Minimize the Possible Risk of Future Epidemics [2023, SpringerBriefs in Applied Sciences and Technology]

Medical Image Classification / Image Annotation

Surveys:

A comprehensive survey on deep active learning and its applications in medical image analysis [2023]

Works:

Fine-tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally [CVPR, 2017]: The CNN is fine-tuned in each active learning iteration incrementally. Assume that each candidate (predefined) takes one of possible labels Y. This assumption might make it more difficult to generalize.
Semi-Supervised Active Learning for COVID-19 Lung Ultrasound Multi-symptom Classification [2020]: Multi-label classification network (MSML) is proposed, and a human-machine interaction is exploited to confirm the final annotations that are used to fine-tune MSML with progressively labeled data. Still pool based.
An Adaptive Low-Rank Modeling-Based Active Learning Method for Medical Image Annotation [2020, IRBM]: Medical images: the intrinsic presence of noise in medical images, the large number of images, and the variety of imaging modalities. Low-rank modeling-based multi-label active learning (LRMMAL) method.
A Transfer Learning Based Active Learning Framework for Brain Tumor Classification [2020]
An Active Learning Method for Diabetic Retinopathy Classification with Uncertainty Quantification [2020]
Deep Reinforcement Active Learning for Medical Image Classification [2020]
MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning [2021]
Representative Region Based Active Learning For Histological Classification Of Colorectal Cancer [2021, ISBI]
Su-Sampling Based Active Learning For Large-Scale Histopathology Image [2021, ICIP]
PathAL: An Active Learning Framework for Histopathology Image Analysis [2021, T-MI]
DECAL: DEployable Clinical Active Learning [2022, ICML Workshop]
Patient Aware Active Learning for Fine-Grained OCT Classification [2022, ICIP]
Information Gain Sampling for Active Learning in Medical Image Classification [2022]
Graph Node Based Interpretability Guided Sample Selection for Active Learning [2022, TMI]
BI-LAVA: Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis [2023, Computer Graphics Forum]
Learning from Incorrectness: Active Learning with Negative Pre-training and Curriculum Querying for Histological Tissue Classification [2023, TMI]
GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification [2024, Medical Image Analysis]

Medical Image Segmentation

Suggestive annotation: A deep active learning framework for biomedical image segmentation [2017, MICCAI]
Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation [2020, IEEE TRANSACTIONS ON MEDICAL IMAGING]
Efficient Active Learning for Image Classification and Segmentation Using a Sample Selection and Conditional Generative Adversarial Network [2018, MICCAI]
Deep Active Learning for Axon-Myelin Segmentation on Histology Data [Arxiv, 2019]
Labeling Cost Sensitive Batch Active Learning For Brain Tumor Segmentation [2021, ISBI]
U-Net-Based Active Learning Framework for Enhancing Cancer Immunotherapy [2021, Master's Thesis]
Active learning for segmentation based on Bayesian sample queries [2021, KBS]
Reducing Annotating Load: Active Learning with Synthetic Images in Surgical Instrument Segmentation [2021]
HAL-IA: A Hybrid Active Learning framework using Interactive Annotation for medical image segmentation [2023, Medical Image Analysis]
EdgeAL: An Edge Estimation Based Active Learning Approach for OCT Segmentation [2023, MICCAI]
A dynamic interactive learning framework for automated 3D medical image segmentation [2023]
BDAL: Balanced Distribution Active Learning for MRI Cardiac Multistructures Segmentation [2024, IEEE Trans. Ind. Inform.]

Medical Symptom Recognition from Text

Medical symptom recognition from patient text: An active learning approach for long-tailed multilabel distributions [2020, ML4H]

ECG Classification

Deep learning approach for active classification of electrocardiogram signals [2016, Information Science]: (280 citations)

Retinal Image Analysis

Human-in-the-loop for efficient training of retinal image analysis methods [2021, Master's Thesis]

Biomedical Knowledge Base Construction

BioAct: Biomedical Knowledge Base Construction using Active Learning [2022]

Mental Disorder Therapy

Explainable Deep Attention Active Learning for Sentimental Analytics of Mental Disorder [2022, TALLIP]

Mobile Health Monitoring / Disease detection

Collaborative Multi-Expert Active Learning for Mobile Health Monitoring: Architecture, Algorithms, and Evaluation [2020, Sensors]
Exploiting Active Learning in Novel Refractive Error Detection with Smartphones [2020, ACMMM]: Use their own created dataset (only 172 images).

Person Re-identification

Rethinking data collection for person re-identification: active redundancy reduction [2021, Pattern Recognition]
Deep Batch Active Learning and Knowledge Distillation for Person Re-identification [2022, IEEE Sensors Journal]
Highly Efficient Active Learning With Tracklet-Aware Co-Cooperative Annotators for Person Re-Identification [2023, TNNLS]

Privacy Policy Classification

Deep Active Learning with Crowdsourcing Data for Privacy Policy Classification [2020, Arxiv]

Questionnaire

Vexation-Aware Active Learning for On-Menu Restaurant Dish Availability [2022, KDD]

Remote Sensing

Remote sensing is an field with really high labeling cost. AL is well used in this field.

Several surveys of AL for remote sensing:

A survey of active learning algorithms for supervised remote sensing image classification
A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data [2021]