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 41–50 of 3073 papers

TitleStatusHype
ActiveNeRF: Learning where to See with Uncertainty EstimationCode1
A2-LINK: Recognizing Disguised Faces via Active Learning and Adversarial Noise based Inter-Domain KnowledgeCode1
Active Learning Helps Pretrained Models Learn the Intended TaskCode1
A Comparative Survey of Deep Active LearningCode1
Active Learning from the WebCode1
Active Learning Meets Optimized Item SelectionCode1
Active learning for medical image segmentation with stochastic batchesCode1
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite ImagesCode1
Active Learning Strategies for Weakly-supervised Object DetectionCode1
Active Learning for Open-set AnnotationCode1
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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