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 1–50 of 3073 papers

TitleStatusHype
Active Learning for Neural PDE SolversCode5
On the reusability of samples in active learningCode5
ActiveAnno3D -- An Active Learning Framework for Multi-Modal 3D Object DetectionCode4
Let's Verify Step by StepCode4
From Easy to Hard: Progressive Active Learning Framework for Infrared Small Target Detection with Single Point SupervisionCode3
DeepCore: A Comprehensive Library for Coreset Selection in Deep LearningCode2
Physics-informed active learning for accelerating quantum chemical simulationsCode2
DCoM: Active Learning for All LearnersCode2
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlowCode2
MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical ImagesCode2
Streaming Active Learning with Deep Neural NetworksCode2
Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AICode2
AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three WeeksCode2
Active Prompting with Chain-of-Thought for Large Language ModelsCode2
Generative Active Learning for Long-tailed Instance SegmentationCode2
Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theoryCode2
DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical ImagesCode2
Transductive Active Learning: Theory and ApplicationsCode2
Multi-Fidelity Active Learning with GFlowNetsCode2
Sailing AI by the Stars: A Survey of Learning from Rewards in Post-Training and Test-Time Scaling of Large Language ModelsCode2
Video Annotator: A framework for efficiently building video classifiers using vision-language models and active learningCode2
ActiveRAG: Autonomously Knowledge Assimilation and Accommodation through Retrieval-Augmented AgentsCode2
Active Generalized Category DiscoveryCode2
ALBench: A Framework for Evaluating Active Learning in Object DetectionCode2
Human-in-the-Loop Large-Scale Predictive Maintenance of WorkstationsCode2
Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary DataCode2
Efficiently Learning at Test-Time: Active Fine-Tuning of LLMsCode2
EFFOcc: A Minimal Baseline for EFficient Fusion-based 3D Occupancy NetworkCode2
POTATO: The Portable Text Annotation ToolCode2
Active Pointly-Supervised Instance SegmentationCode1
A Benchmark on Uncertainty Quantification for Deep Learning PrognosticsCode1
Enhanced spatio-temporal electric load forecasts using less data with active deep learningCode1
ActiveNeRF: Learning where to See with Uncertainty EstimationCode1
Active Prompt Learning in Vision Language ModelsCode1
Active Learning Through a Covering LensCode1
Active learning with MaskAL reduces annotation effort for training Mask R-CNNCode1
Active Learning on a Budget: Opposite Strategies Suit High and Low BudgetsCode1
Active Learning from the WebCode1
Active Learning Meets Optimized Item SelectionCode1
Active Learning Strategies for Weakly-supervised Object DetectionCode1
Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-TrainingCode1
A2-LINK: Recognizing Disguised Faces via Active Learning and Adversarial Noise based Inter-Domain KnowledgeCode1
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite ImagesCode1
A Comparative Survey of Deep Active LearningCode1
Active Learning for Deep Object Detection via Probabilistic ModelingCode1
Active Learning for Domain Adaptation: An Energy-Based ApproachCode1
Active learning for medical image segmentation with stochastic batchesCode1
Active Learning Helps Pretrained Models Learn the Intended TaskCode1
Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)Code1
Active Learning for Computationally Efficient Distribution of Binary Evolution SimulationsCode1
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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