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 26–50 of 3073 papers

TitleStatusHype
Multi-Fidelity Active Learning with GFlowNetsCode2
AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three WeeksCode2
Streaming Active Learning with Deep Neural NetworksCode2
Active Prompting with Chain-of-Thought for Large Language ModelsCode2
A Benchmark on Uncertainty Quantification for Deep Learning PrognosticsCode1
Active Learning at the ImageNet ScaleCode1
Active Pointly-Supervised Instance SegmentationCode1
Active Prompt Learning in Vision Language ModelsCode1
Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-TrainingCode1
Active learning with MaskAL reduces annotation effort for training Mask R-CNNCode1
Enhanced spatio-temporal electric load forecasts using less data with active deep learningCode1
Active Learning Through a Covering LensCode1
Active Imitation Learning with Noisy GuidanceCode1
Active Invariant Causal Prediction: Experiment Selection through StabilityCode1
Active learning based generative design for the discovery of wide bandgap materialsCode1
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