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 2526–2550 of 3073 papers

TitleStatusHype
PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and ClassificationCode0
Understanding Uncertainty SamplingCode0
Aspect-based Sentiment Analysis of Scientific ReviewsCode0
Crowd Counting With Partial Annotations in an ImageCode0
Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local FilterCode0
Deep Bayesian Active Learning for Accelerating Stochastic SimulationCode0
MCAL: Minimum Cost Human-Machine Active LabelingCode0
How to Allocate your Label Budget? Choosing between Active Learning and Learning to Reject in Anomaly DetectionCode0
Mining GOLD Samples for Conditional GANsCode0
CrudeOilNews: An Annotated Crude Oil News Corpus for Event ExtractionCode0
Wide Contextual Residual Network with Active Learning for Remote Sensing Image ClassificationCode0
Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition CharacteristicsCode0
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian ProcessesCode0
Curiosity Driven Exploration to Optimize Structure-Property Learning in MicroscopyCode0
Performance of Machine Learning Classifiers for Anomaly Detection in Cyber Security ApplicationsCode0
How useful is Active Learning for Image-based Plant Phenotyping?Code0
Automated Progressive Red TeamingCode0
Data augmentation on-the-fly and active learning in data stream classificationCode0
Context Selection and Rewriting for Video-based Educational Question GenerationCode0
Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic QuantitiesCode0
Test-time augmentation-based active learning and self-training for label-efficient segmentationCode0
Constraining the Parameters of High-Dimensional Models with Active LearningCode0
Adversarial Distillation of Bayesian Neural Network PosteriorsCode0
Towards Efficient Active Learning of PDFACode0
Unique Rashomon Sets for Robust Active LearningCode0
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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