SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–60 of 3073 papers

TitleStatusHype
Active Learning at the ImageNet ScaleCode1
Active learning based generative design for the discovery of wide bandgap materialsCode1
Active Learning by Acquiring Contrastive ExamplesCode1
Active learning with MaskAL reduces annotation effort for training Mask R-CNNCode1
Active Learning on a Budget: Opposite Strategies Suit High and Low BudgetsCode1
Active Learning for BERT: An Empirical StudyCode1
Active Learning for Bayesian 3D Hand Pose EstimationCode1
Active Testing: Sample-Efficient Model EvaluationCode1
A Benchmark on Uncertainty Quantification for Deep Learning PrognosticsCode1
Active Learning Strategies for Weakly-supervised Object DetectionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified