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 276–300 of 3073 papers

TitleStatusHype
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