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 151–175 of 3073 papers

TitleStatusHype
Bayesian Active Learning with Fully Bayesian Gaussian ProcessesCode1
Self-supervised Assisted Active Learning for Skin Lesion SegmentationCode1
Towards Computationally Feasible Deep Active LearningCode1
Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study)Code1
Active Learning Helps Pretrained Models Learn the Intended TaskCode1
Graph-based Active Learning for Semi-supervised Classification of SAR DataCode1
Active Learning for Computationally Efficient Distribution of Binary Evolution SimulationsCode1
A Comparative Survey of Deep Active LearningCode1
A Framework and Benchmark for Deep Batch Active Learning for RegressionCode1
Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine SynergyCode1
Active Learning by Feature MixingCode1
Learning Distinctive Margin toward Active Domain AdaptationCode1
Optical Flow Training under Limited Label Budget via Active LearningCode1
BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active AnnotationCode1
Neural Galerkin Schemes with Active Learning for High-Dimensional Evolution EquationsCode1
Biological Sequence Design with GFlowNetsCode1
Information Gain Propagation: a new way to Graph Active Learning with Soft LabelsCode1
FAMIE: A Fast Active Learning Framework for Multilingual Information ExtractionCode1
D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic SegmentationCode1
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model EvaluationCode1
Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte CarloCode1
Active Learning on a Budget: Opposite Strategies Suit High and Low BudgetsCode1
PT4AL: Using Self-Supervised Pretext Tasks for Active LearningCode1
Active Learning for Open-set AnnotationCode1
CitySurfaces: City-Scale Semantic Segmentation of Sidewalk MaterialsCode1
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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