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
Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary DataCode2
Efficiently Learning at Test-Time: Active Fine-Tuning of LLMsCode2
EFFOcc: A Minimal Baseline for EFficient Fusion-based 3D Occupancy NetworkCode2
POTATO: The Portable Text Annotation ToolCode2
Active Pointly-Supervised Instance SegmentationCode1
A Benchmark on Uncertainty Quantification for Deep Learning PrognosticsCode1
Enhanced spatio-temporal electric load forecasts using less data with active deep learningCode1
ActiveNeRF: Learning where to See with Uncertainty EstimationCode1
Active Prompt Learning in Vision Language ModelsCode1
Active Learning Through a Covering LensCode1
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 from the WebCode1
Active Learning Meets Optimized Item SelectionCode1
Active Learning Strategies for Weakly-supervised Object DetectionCode1
Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-TrainingCode1
A2-LINK: Recognizing Disguised Faces via Active Learning and Adversarial Noise based Inter-Domain KnowledgeCode1
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite ImagesCode1
A Comparative Survey of Deep Active LearningCode1
Active Learning for Deep Object Detection via Probabilistic ModelingCode1
Active Learning for Domain Adaptation: An Energy-Based ApproachCode1
Active learning for medical image segmentation with stochastic batchesCode1
Active Learning Helps Pretrained Models Learn the Intended TaskCode1
Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)Code1
Active Learning for Computationally Efficient Distribution of Binary Evolution SimulationsCode1
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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