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 151–160 of 3073 papers

TitleStatusHype
Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active LearningCode1
A deep active learning system for species identification and counting in camera trap imagesCode1
Active Bayesian Causal InferenceCode1
Differentiable sampling of molecular geometries with uncertainty-based adversarial attacksCode1
Active Imitation Learning with Noisy GuidanceCode1
Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation EffortsCode1
Active learning for medical image segmentation with stochastic batchesCode1
Active Learning at the ImageNet ScaleCode1
Active Learning by Acquiring Contrastive ExamplesCode1
Active Learning for Deep Object Detection via Probabilistic ModelingCode1
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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