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 1341–1350 of 3073 papers

Title	Date	Tasks	Status	Hype
Safe Active Learning for Multi-Output Gaussian Processes	Mar 28, 2022	Active LearningGaussian Processes	CodeCode Available	0
Investigating Active-learning-based Training Data Selection for Speech Spoofing Countermeasure	Mar 28, 2022	Active LearningData Augmentation	—Unverified	0
A Comparative Survey of Deep Active Learning	Mar 25, 2022	Active LearningSurvey	CodeCode Available	1
MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images	Mar 23, 2022	Active Learning	CodeCode Available	2
Frugal Learning of Virtual Exemplars for Label-Efficient Satellite Image Change Detection	Mar 22, 2022	Active LearningChange Detection	—Unverified	0
Reinforcement-based frugal learning for satellite image change detection	Mar 22, 2022	Active LearningChange Detection	—Unverified	0
Semantic Segmentation with Active Semi-Supervised Learning	Mar 21, 2022	Active LearningSegmentation	—Unverified	0
Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications	Mar 21, 2022	Active LearningDecision Making	—Unverified	0
RareGAN: Generating Samples for Rare Classes	Mar 20, 2022	Active LearningDiversity	CodeCode Available	0
Active learning in open experimental environments: selecting the right information channel(s) based on predictability in deep kernel learning	Mar 18, 2022	Active Learning	CodeCode Available	0

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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