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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 181–190 of 3073 papers

Title	Date	Tasks	Status	Hype
Boosting Active Learning via Improving Test Performance	Dec 10, 2021	Active LearningElectron Tomography	CodeCode Available	1
Active Sensing for Communications by Learning	Dec 8, 2021	Active Learning	CodeCode Available	1
A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samples	Dec 3, 2021	Active LearningDeep Learning	CodeCode Available	1
Active Learning for Domain Adaptation: An Energy-Based Approach	Dec 2, 2021	Active LearningDomain Adaptation	CodeCode Available	1
TALISMAN: Targeted Active Learning for Object Detection with Rare Classes and Slices using Submodular Mutual Information	Nov 30, 2021	Active LearningAutonomous Vehicles	CodeCode Available	1
DeepAL: Deep Active Learning in Python	Nov 30, 2021	Active Learning	CodeCode Available	1
Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation	Nov 25, 2021	Active LearningPrediction	CodeCode Available	1
Active Learning at the ImageNet Scale	Nov 25, 2021	Active Learning	CodeCode Available	1
Active Learning Meets Optimized Item Selection	Nov 22, 2021	Active LearningClustering	CodeCode Available	1
Fink: early supernovae Ia classification using active learning	Nov 22, 2021	Active LearningClassification	CodeCode Available	1

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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