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 126–150 of 3073 papers

TitleStatusHype
Adaptive Superpixel for Active Learning in Semantic SegmentationCode1
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model EvaluationCode1
Accelerating high-throughput virtual screening through molecular pool-based active learningCode1
Active Testing: Sample-Efficient Model EvaluationCode1
D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic SegmentationCode1
AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African LanguagesCode1
Enhanced spatio-temporal electric load forecasts using less data with active deep learningCode1
Active learning with MaskAL reduces annotation effort for training Mask R-CNNCode1
ActiveNeRF: Learning where to See with Uncertainty EstimationCode1
Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)Code1
Active Learning Meets Optimized Item SelectionCode1
Active Learning on a Budget: Opposite Strategies Suit High and Low BudgetsCode1
Active Learning from the WebCode1
Active Learning Through a Covering LensCode1
Active Invariant Causal Prediction: Experiment Selection through StabilityCode1
Active Learning Helps Pretrained Models Learn the Intended TaskCode1
Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-TrainingCode1
Active Learning Strategies for Weakly-supervised Object DetectionCode1
Active Prompt Learning in Vision Language ModelsCode1
Active Sensing for Communications by LearningCode1
Active Statistical InferenceCode1
Active Test-Time Adaptation: Theoretical Analyses and An AlgorithmCode1
Active Transfer Learning for Efficient Video-Specific Human Pose EstimationCode1
Active Anomaly Detection via EnsemblesCode1
Active Pointly-Supervised Instance SegmentationCode1
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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