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 101–110 of 3073 papers

Title	Date	Tasks	Status	Hype
Box-Level Active Detection	Mar 23, 2023	Active Learningobject-detection	CodeCode Available	1
Re-thinking Federated Active Learning based on Inter-class Diversity	Mar 22, 2023	Active LearningDiversity	CodeCode Available	1
ReBound: An Open-Source 3D Bounding Box Annotation Tool for Active Learning	Mar 11, 2023	3D Object DetectionActive Learning	CodeCode Available	1
Dirichlet-based Uncertainty Calibration for Active Domain Adaptation	Feb 27, 2023	Active LearningDomain Adaptation	CodeCode Available	1
A Benchmark on Uncertainty Quantification for Deep Learning Prognostics	Feb 9, 2023	Active LearningDecision Making	CodeCode Available	1
Continuous Learning for Android Malware Detection	Feb 8, 2023	Active LearningAndroid Malware Detection	CodeCode Available	1
An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping	Feb 7, 2023	Active LearningSemantic Segmentation	CodeCode Available	1
SAAL: Sharpness-Aware Active Learning	Feb 2, 2023	Active Learningimage-classification	CodeCode Available	1
Iterative Loop Method Combining Active and Semi-Supervised Learning for Domain Adaptive Semantic Segmentation	Jan 31, 2023	Active LearningDomain Adaptation	CodeCode Available	1
Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessment	Jan 25, 2023	Active Learningimage-classification	CodeCode Available	1

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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