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 1111–1120 of 3073 papers

Title	Date	Tasks	Status
Automatic Analysis of the Emotional Content of Speech in Daylong Child-Centered Recordings from a Neonatal Intensive Care Unit	Jun 14, 2021	Active LearningBinary Classification	—Unverified
Active Learning for Deep Neural Networks on Edge Devices	Jun 21, 2021	Active Learningobject-detection	—Unverified
Active Domain Adaptation with Multi-level Contrastive Units for Semantic Segmentation	May 23, 2022	Active LearningDomain Adaptation	—Unverified
Automated Testing of Spatially-Dependent Environmental Hypotheses through Active Transfer Learning	Feb 28, 2024	Active LearningTransfer Learning	—Unverified
Active Learning with Expert Advice	Sep 26, 2013	Active Learning	—Unverified
Comprehensive Evaluation of OCT-based Automated Segmentation of Retinal Layer, Fluid and Hyper-Reflective Foci: Impact on Diabetic Retinopathy Severity Assessment	Mar 3, 2025	Active LearningSegmentation	—Unverified
Active Learning with Efficient Feature Weighting Methods for Improving Data Quality and Classification Accuracy	Jun 1, 2014	Active LearningGeneral Classification	—Unverified
Automated Neural Patent Landscaping in the Small Data Regime	Jul 10, 2024	Active Learning	—Unverified
Automated Image Analysis Framework for the High-Throughput Determination of Grapevine Berry Sizes Using Conditional Random Fields	Dec 15, 2017	Active LearningClassification	—Unverified
Automated Gain Control Through Deep Reinforcement Learning for Downstream Radar Object Detection	Jul 8, 2021	Active LearningDeep Reinforcement Learning	—Unverified

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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