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
Box-Level Active DetectionCode1
Re-thinking Federated Active Learning based on Inter-class DiversityCode1
ReBound: An Open-Source 3D Bounding Box Annotation Tool for Active LearningCode1
Dirichlet-based Uncertainty Calibration for Active Domain AdaptationCode1
A Benchmark on Uncertainty Quantification for Deep Learning PrognosticsCode1
Continuous Learning for Android Malware DetectionCode1
An Informative Path Planning Framework for Active Learning in UAV-based Semantic MappingCode1
SAAL: Sharpness-Aware Active LearningCode1
Iterative Loop Method Combining Active and Semi-Supervised Learning for Domain Adaptive Semantic SegmentationCode1
Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance AssessmentCode1
Exploring Active 3D Object Detection from a Generalization PerspectiveCode1
Active learning for medical image segmentation with stochastic batchesCode1
MoBYv2AL: Self-supervised Active Learning for Image ClassificationCode1
Are Binary Annotations Sufficient? Video Moment Retrieval via Hierarchical Uncertainty-Based Active LearningCode1
Man-recon: manifold learning for reconstruction with deep autoencoder for smart seismic interpretationCode1
MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object DetectionCode1
PyTAIL: Interactive and Incremental Learning of NLP Models with Human in the Loop for Online DataCode1
Knowledge-Aware Federated Active Learning with Non-IID DataCode1
Plug and Play Active Learning for Object DetectionCode1
Finding active galactic nuclei through FinkCode1
Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regressionCode1
LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic SegmentationCode1
AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African LanguagesCode1
Materials Property Prediction with Uncertainty Quantification: A Benchmark StudyCode1
Fast and robust Bayesian Inference using Gaussian Processes with GPryCode1
Fine-Tuning Language Models via Epistemic Neural NetworksCode1
cRedAnno+: Annotation Exploitation in Self-Explanatory Lung Nodule DiagnosisCode1
Provable Safe Reinforcement Learning with Binary FeedbackCode1
Multi-Objective GFlowNetsCode1
Bayesian Optimization with Conformal Prediction SetsCode1
A Survey of Dataset Refinement for Problems in Computer Vision DatasetsCode1
Active Learning from the WebCode1
Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active LearningCode1
VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose EstimationCode1
Efficient Bayesian Updates for Deep Learning via Laplace ApproximationsCode1
Making Your First Choice: To Address Cold Start Problem in Vision Active LearningCode1
ActiveNeRF: Learning where to See with Uncertainty EstimationCode1
Active Learning for Optimal Intervention Design in Causal ModelsCode1
BenchPress: A Deep Active Benchmark GeneratorCode1
A Holistic Approach to Undesired Content Detection in the Real WorldCode1
CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume SegmentationCode1
Active Learning Strategies for Weakly-supervised Object DetectionCode1
Active Pointly-Supervised Instance SegmentationCode1
Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image SegmentationCode1
AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language ModelsCode1
Deep Active Learning for Regression Using ε-weighted Hybrid Query StrategyCode1
ICS: Total Freedom in Manual Text Classification Supported by Unobtrusive Machine LearningCode1
Active Bayesian Causal InferenceCode1
PyRelationAL: a python library for active learning research and developmentCode1
Active Learning Through a Covering LensCode1
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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