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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 141–150 of 3073 papers

Title	Date	Tasks	Status	Hype
Active Learning Helps Pretrained Models Learn the Intended Task	Apr 18, 2022	Active Learning	CodeCode Available	1
Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-Training	Dec 27, 2021	3D Pose EstimationActive Learning	CodeCode Available	1
Active Learning Strategies for Weakly-supervised Object Detection	Jul 25, 2022	Active LearningObject	CodeCode Available	1
Active Prompt Learning in Vision Language Models	Nov 18, 2023	Active LearningPrompt Learning	CodeCode Available	1
Active Sensing for Communications by Learning	Dec 8, 2021	Active Learning	CodeCode Available	1
Active Statistical Inference	Mar 5, 2024	Active Learningvalid	CodeCode Available	1
Active Test-Time Adaptation: Theoretical Analyses and An Algorithm	Apr 7, 2024	Active LearningDomain Adaptation	CodeCode Available	1
Active Transfer Learning for Efficient Video-Specific Human Pose Estimation	Nov 8, 2023	Active LearningPose Estimation	CodeCode Available	1
Active Anomaly Detection via Ensembles	Sep 17, 2018	Active LearningAnomaly Detection	CodeCode Available	1
Active Pointly-Supervised Instance Segmentation	Jul 23, 2022	Active LearningInstance Segmentation	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