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 151–200 of 3073 papers

TitleStatusHype
Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active LearningCode1
Active Learning from the WebCode1
Active Bayesian Causal InferenceCode1
Active Learning Helps Pretrained Models Learn the Intended TaskCode1
Active Learning Meets Optimized Item SelectionCode1
Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)Code1
Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine SynergyCode1
Active Learning at the ImageNet ScaleCode1
Active Learning Strategies for Weakly-supervised Object DetectionCode1
Active learning with MaskAL reduces annotation effort for training Mask R-CNNCode1
Active Learning Through a Covering LensCode1
Active learning based generative design for the discovery of wide bandgap materialsCode1
Consistency-based Active Learning for Object DetectionCode1
Contextual Diversity for Active LearningCode1
Bayesian Optimization with Conformal Prediction SetsCode1
Counting People by Estimating People FlowsCode1
Active Learning for Deep Object Detection via Probabilistic ModelingCode1
Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-TrainingCode1
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical SimulationsCode1
Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regressionCode1
Active Learning by Acquiring Contrastive ExamplesCode1
Active Learning by Feature MixingCode1
DEAL: Deep Evidential Active Learning for Image ClassificationCode1
DEAL: Difficulty-aware Active Learning for Semantic SegmentationCode1
Active Imitation Learning with Noisy GuidanceCode1
A Simple Baseline for Low-Budget Active LearningCode1
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property PredictionCode1
Active Prompt Learning in Vision Language ModelsCode1
A Comparative Survey of Deep Active LearningCode1
Active Learning for Domain Adaptation: An Energy-Based ApproachCode1
DeepAL: Deep Active Learning in PythonCode1
Active Sensing for Communications by LearningCode1
A2-LINK: Recognizing Disguised Faces via Active Learning and Adversarial Noise based Inter-Domain KnowledgeCode1
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model EvaluationCode1
Stochastic Batch Acquisition: A Simple Baseline for Deep Active LearningCode1
DeepDrummer : Generating Drum Loops using Deep Learning and a Human in the LoopCode1
Active Transfer Learning for Efficient Video-Specific Human Pose EstimationCode1
Detecting Underspecification with Local EnsemblesCode1
AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language ModelsCode1
Active WeaSuL: Improving Weak Supervision with Active LearningCode1
D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic SegmentationCode1
Adaptive Superpixel for Active Learning in Semantic SegmentationCode1
Active Learning for Bayesian 3D Hand Pose EstimationCode1
Active Learning for BERT: An Empirical StudyCode1
Open Source Software for Efficient and Transparent ReviewsCode1
Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center DatasetCode1
A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experimentsCode1
Divide and Adapt: Active Domain Adaptation via Customized LearningCode1
Active Learning for Computationally Efficient Distribution of Binary Evolution SimulationsCode1
Active Learning for Convolutional Neural Networks: A Core-Set ApproachCode1
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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