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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 981–990 of 3073 papers

Title	Date	Tasks	Status	Hype
Best Practices in Active Learning for Semantic Segmentation	Feb 8, 2023	Active LearningSegmentation	—Unverified	0
Continuous Learning for Android Malware Detection	Feb 8, 2023	Active LearningAndroid Malware Detection	CodeCode Available	1
CRL+: A Novel Semi-Supervised Deep Active Contrastive Representation Learning-Based Text Classification Model for Insurance Data	Feb 8, 2023	Active LearningRepresentation Learning	—Unverified	0
Combining self-labeling and demand based active learning for non-stationary data streams	Feb 8, 2023	Active Learningvalid	—Unverified	0
AutoWS: Automated Weak Supervision Framework for Text Classification	Feb 7, 2023	Active LearningClassification	—Unverified	0
An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping	Feb 7, 2023	Active LearningSemantic Segmentation	CodeCode Available	1
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization	Feb 5, 2023	Active LearningBrain Tumor Segmentation	—Unverified	0
SAAL: Sharpness-Aware Active Learning	Feb 2, 2023	Active Learningimage-classification	CodeCode Available	1
A Semi-Parametric Model for Decision Making in High-Dimensional Sensory Discrimination Tasks	Feb 2, 2023	Active LearningDecision Making	—Unverified	0
Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling	Feb 2, 2023	Active Learning	CodeCode Available	0

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Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	TypiClust	Accuracy	93.2	—	Unverified
2	PT4AL	Accuracy	93.1	—	Unverified
3	Learning loss	Accuracy	91.01	—	Unverified
4	CoreGCN	Accuracy	90.7	—	Unverified
5	Core-set	Accuracy	89.92	—	Unverified
6	Random Baseline (Resnet18)	Accuracy	88.45	—	Unverified
7	Random Baseline (VGG16)	Accuracy	85.09	—	Unverified