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 1181–1190 of 3073 papers

Title	Date	Tasks	Status
Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Budget	Sep 25, 2019	Active LearningRepresentation Learning	—Unverified
Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation	Apr 3, 2023	Active LearningTransfer Learning	—Unverified
CREStE: Scalable Mapless Navigation with Internet Scale Priors and Counterfactual Guidance	Mar 5, 2025	Active Learningcounterfactual	—Unverified
Accelerating engineering design by automatic selection of simulation cases through Pool-Based Active Learning	Sep 3, 2020	Active Learning	—Unverified
Critic Loss for Image Classification	Sep 23, 2024	Active LearningClassification	—Unverified
CRL+: A Novel Semi-Supervised Deep Active Contrastive Representation Learning-Based Text Classification Model for Insurance Data	Feb 8, 2023	Active LearningRepresentation Learning	—Unverified
Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods	Oct 18, 2013	Active LearningClassification Of Hyperspectral Images	—Unverified
Active learning for regression in engineering populations: A risk-informed approach	Sep 6, 2024	Active Learningregression	—Unverified
Cross-lingual German Biomedical Information Extraction: from Zero-shot to Human-in-the-Loop	Jan 24, 2023	Active LearningTransfer Learning	—Unverified
Data Uncertainty without Prediction Models	Apr 25, 2022	Active LearningPrediction	—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