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 251–300 of 3073 papers

TitleStatusHype
TagRuler: Interactive Tool for Span-Level Data Programming by DemonstrationCode1
Active Learning for Human-in-the-Loop Customs InspectionCode1
Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain ShiftsCode1
Towards Balanced Active Learning for Multimodal ClassificationCode1
Learning Loss for Active LearningCode1
Active Learning on a Budget: Opposite Strategies Suit High and Low BudgetsCode1
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite ImagesCode1
ActiveGLAE: A Benchmark for Deep Active Learning with TransformersCode1
Active Learning Through a Covering LensCode1
Active Learning Meets Optimized Item SelectionCode1
Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-TrainingCode1
Enhanced spatio-temporal electric load forecasts using less data with active deep learningCode1
Active Prompt Learning in Vision Language ModelsCode1
Active Learning Strategies for Weakly-supervised Object DetectionCode1
Active Statistical InferenceCode1
Active learning for medical image segmentation with stochastic batchesCode1
Active Imitation Learning with Noisy GuidanceCode1
Active Test-Time Adaptation: Theoretical Analyses and An AlgorithmCode1
Active WeaSuL: Improving Weak Supervision with Active LearningCode1
AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language ModelsCode1
Active Invariant Causal Prediction: Experiment Selection through StabilityCode1
Differentiable sampling of molecular geometries with uncertainty-based adversarial attacksCode1
A Framework and Benchmark for Deep Batch Active Learning for RegressionCode1
AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African LanguagesCode1
AISecKG: Knowledge Graph Dataset for Cybersecurity EducationCode1
Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center DatasetCode1
A-LINK: Recognizing Disguised Faces via Active Learning based Inter-Domain KnowledgeCode1
All you need are a few pixels: semantic segmentation with PixelPickCode1
An Informative Path Planning Framework for Active Learning in UAV-based Semantic MappingCode1
Are Binary Annotations Sufficient? Video Moment Retrieval via Hierarchical Uncertainty-Based Active LearningCode1
Stochastic Batch Acquisition: A Simple Baseline for Deep Active LearningCode1
A Simple Baseline for Low-Budget Active LearningCode1
Bayesian Active Learning with Fully Bayesian Gaussian ProcessesCode1
A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled SamplesCode1
AcTune: Uncertainty-aware Active Self-Training for Semi-Supervised Active Learning with Pretrained Language ModelsCode1
Active Learning for Open-set AnnotationCode1
Active Learning for Optimal Intervention Design in Causal ModelsCode1
Accelerating high-throughput virtual screening through molecular pool-based active learningCode1
Bayesian Model-Agnostic Meta-LearningCode1
Bayesian Optimization with Conformal Prediction SetsCode1
Biological Sequence Design with GFlowNetsCode1
Boosting Active Learning via Improving Test PerformanceCode1
Box-Level Active DetectionCode1
Building a Scalable and Interpretable Bayesian Deep Learning Framework for Quality Control of Free Form SurfacesCode1
Causal-Guided Active Learning for Debiasing Large Language ModelsCode1
ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular GenerationCode1
CitySurfaces: City-Scale Semantic Segmentation of Sidewalk MaterialsCode1
Class-Balanced Active Learning for Image ClassificationCode1
Active Anomaly Detection via EnsemblesCode1
Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regressionCode1
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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