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 101–125 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
Show:102550
← PrevPage 5 of 123Next →

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