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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 591–600 of 3073 papers

Title	Date	Tasks	Status	Hype
Quantifying Policy Administration Cost in an Active Learning Framework	Dec 29, 2023	Active Learning	—Unverified	0
Reinforcement-based Display-size Selection for Frugal Satellite Image Change Detection	Dec 28, 2023	Active LearningChange Detection	—Unverified	0
DeLR: Active Learning for Detection with Decoupled Localization and Recognition Query	Dec 28, 2023	Active LearningObject	—Unverified	0
Active Third-Person Imitation Learning	Dec 27, 2023	Active LearningGenerative Adversarial Network	—Unverified	0
BAL: Balancing Diversity and Novelty for Active Learning	Dec 26, 2023	Active LearningDiversity	CodeCode Available	0
Optimal Decision Tree and Adaptive Submodular Ranking with Noisy Outcomes	Dec 23, 2023	Active Learning	—Unverified	0
MEAOD: Model Extraction Attack against Object Detectors	Dec 22, 2023	Active Learningmodel	—Unverified	0
Entropic Open-set Active Learning	Dec 21, 2023	Active Learning	CodeCode Available	1
On the Convergence of Loss and Uncertainty-based Active Learning Algorithms	Dec 21, 2023	Active Learning	CodeCode Available	0
Generalized Category Discovery with Large Language Models in the Loop	Dec 18, 2023	Active LearningContrastive Learning	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