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 81–90 of 3073 papers

TitleStatusHype
Active Learning by Feature MixingCode1
Active Transfer Learning for Efficient Video-Specific Human Pose EstimationCode1
Active Learning for Bayesian 3D Hand Pose EstimationCode1
Active Learning for BERT: An Empirical StudyCode1
Active Learning for Convolutional Neural Networks: A Core-Set ApproachCode1
Active Learning for Computationally Efficient Distribution of Binary Evolution SimulationsCode1
Enhanced spatio-temporal electric load forecasts using less data with active deep learningCode1
Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center DatasetCode1
A Comparative Survey of Deep Active LearningCode1
ActiveNeRF: Learning where to See with Uncertainty EstimationCode1
Show:102550
← PrevPage 9 of 308Next →

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