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 101–125 of 3073 papers

TitleStatusHype
A comprehensive survey on deep active learning in medical image analysisCode1
Active Learning Meets Optimized Item SelectionCode1
Active Learning Strategies for Weakly-supervised Object DetectionCode1
Bayesian Model-Agnostic Meta-LearningCode1
Active Learning on a Budget: Opposite Strategies Suit High and Low BudgetsCode1
Active Domain Adaptation via Clustering Uncertainty-weighted EmbeddingsCode1
Biological Sequence Design with GFlowNetsCode1
Active Learning Through a Covering LensCode1
BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active AnnotationCode1
Box-Level Active DetectionCode1
Active Learning for Bayesian 3D Hand Pose EstimationCode1
D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic SegmentationCode1
Causal-Guided Active Learning for Debiasing Large Language ModelsCode1
ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular GenerationCode1
Active learning with MaskAL reduces annotation effort for training Mask R-CNNCode1
Class-Balanced Active Learning for Image ClassificationCode1
CloudS2Mask: A novel deep learning approach for improved cloud and cloud shadow masking in Sentinel-2 imageryCode1
Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-TrainingCode1
A Benchmark on Uncertainty Quantification for Deep Learning PrognosticsCode1
Enhanced spatio-temporal electric load forecasts using less data with active deep learningCode1
COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image SegmentationCode1
Conditioning Sparse Variational Gaussian Processes for Online Decision-makingCode1
Consistency-based Active Learning for Object DetectionCode1
Active Anomaly Detection via EnsemblesCode1
Active Learning by Acquiring Contrastive ExamplesCode1
Show:102550
← PrevPage 5 of 123Next →

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