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 101–150 of 3073 papers

TitleStatusHype
A comprehensive survey on deep active learning in medical image analysisCode1
Conditioning Sparse Variational Gaussian Processes for Online Decision-makingCode1
Enhanced spatio-temporal electric load forecasts using less data with active deep learningCode1
Contextual Diversity for Active LearningCode1
Counting People by Estimating People FlowsCode1
Active Domain Adaptation via Clustering Uncertainty-weighted EmbeddingsCode1
Active Pointly-Supervised Instance SegmentationCode1
CriticLean: Critic-Guided Reinforcement Learning for Mathematical FormalizationCode1
ActiveNeRF: Learning where to See with Uncertainty EstimationCode1
Unsupervised Selective Labeling for More Effective Semi-Supervised LearningCode1
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property PredictionCode1
Active Prompt Learning in Vision Language ModelsCode1
Are Binary Annotations Sufficient? Video Moment Retrieval via Hierarchical Uncertainty-Based Active LearningCode1
DEAL: Difficulty-aware Active Learning for Semantic SegmentationCode1
Active Sensing for Communications by LearningCode1
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model EvaluationCode1
Deep Active Learning for Joint Classification & Segmentation with Weak AnnotatorCode1
Active Statistical InferenceCode1
A Benchmark on Uncertainty Quantification for Deep Learning PrognosticsCode1
Active Testing: Sample-Efficient Model EvaluationCode1
Active Test-Time Adaptation: Theoretical Analyses and An AlgorithmCode1
Active Transfer Learning for Efficient Video-Specific Human Pose EstimationCode1
Stochastic Batch Acquisition: A Simple Baseline for Deep Active LearningCode1
A Mathematical Analysis of Learning Loss for Active Learning in RegressionCode1
Active learning for medical image segmentation with stochastic batchesCode1
AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language ModelsCode1
AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced DatasetsCode1
A Simple Baseline for Low-Budget Active LearningCode1
AcTune: Uncertainty-aware Active Self-Training for Semi-Supervised Active Learning with Pretrained Language ModelsCode1
AISecKG: Knowledge Graph Dataset for Cybersecurity EducationCode1
A Holistic Approach to Undesired Content Detection in the Real WorldCode1
AL-GTD: Deep Active Learning for Gaze Target DetectionCode1
A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experimentsCode1
Active Learning for Computationally Efficient Distribution of Binary Evolution SimulationsCode1
A Framework and Benchmark for Deep Batch Active Learning for RegressionCode1
Active Learning for Convolutional Neural Networks: A Core-Set ApproachCode1
Active Invariant Causal Prediction: Experiment Selection through StabilityCode1
Active Learning for Deep Object Detection via Probabilistic ModelingCode1
Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation EffortsCode1
Active Learning for Coreference Resolution using Discrete AnnotationCode1
AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African LanguagesCode1
A-LINK: Recognizing Disguised Faces via Active Learning based Inter-Domain KnowledgeCode1
Accelerating high-throughput virtual screening through molecular pool-based active learningCode1
Active Learning for Domain Adaptation: An Energy-Based ApproachCode1
Active Learning for Optimal Intervention Design in Causal ModelsCode1
ALPBench: A Benchmark for Active Learning Pipelines on Tabular DataCode1
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite ImagesCode1
An Informative Path Planning Framework for Active Learning in UAV-based Semantic MappingCode1
Active Anomaly Detection via EnsemblesCode1
D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic SegmentationCode1
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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