Active Learning

Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 311–320 of 3073 papers

Title	Date	Tasks	Status	Hype	Score
AISecKG: Knowledge Graph Dataset for Cybersecurity Education	Mar 30, 2023	Active LearningKnowledge Graphs	CodeCode Available	1	5
Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regression	Nov 16, 2022	Active Learning	CodeCode Available	1	5
Deep Active Learning in Remote Sensing for data efficient Change Detection	Aug 25, 2020	Active LearningChange Detection	CodeCode Available	1	5
Generating π-Functional Molecules Using STGG+ with Active Learning	Feb 20, 2025	Active Learningreinforcement-learning	CodeCode Available	1	5
MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object Detection	Dec 6, 2022	Active LearningActive Object Detection	CodeCode Available	1	5
GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning	Dec 19, 2020	Active Learning	CodeCode Available	1	5
Continual egocentric object recognition	Dec 6, 2019	Active LearningNovelty Detection	CodeCode Available	0	5
Continual Deep Active Learning for Medical Imaging: Replay-Base Architecture for Context Adaptation	Jan 14, 2025	Active Learningclass-incremental learning	CodeCode Available	0	5
Active Generation for Image Classification	Mar 11, 2024	Active LearningClassification	CodeCode Available	0	5
Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics	Nov 25, 2021	Active LearningAge Estimation	CodeCode Available	0	5

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Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	TypiClust	Accuracy	93.2	—	Unverified
2	PT4AL	Accuracy	93.1	—	Unverified
3	Learning loss	Accuracy	91.01	—	Unverified
4	CoreGCN	Accuracy	90.7	—	Unverified
5	Core-set	Accuracy	89.92	—	Unverified
6	Random Baseline (Resnet18)	Accuracy	88.45	—	Unverified
7	Random Baseline (VGG16)	Accuracy	85.09	—	Unverified