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 1131–1140 of 3073 papers

Title	Date	Tasks	Status	Hype
ISEE.U: Distributed online active target localization with unpredictable targets	Oct 17, 2022	Active LearningCPU	—Unverified	0
Semantic Segmentation with Active Semi-Supervised Representation Learning	Oct 16, 2022	Active LearningContrastive Learning	—Unverified	0
Active Learning with Neural Networks: Insights from Nonparametric Statistics	Oct 15, 2022	Active Learning	—Unverified	0
Improving the Intra-class Long-tail in 3D Detection via Rare Example Mining	Oct 15, 2022	3D Object DetectionActive Learning	—Unverified	0
Active Learning from the Web	Oct 15, 2022	Active LearningInformation Retrieval	CodeCode Available	1
GFlowCausal: Generative Flow Networks for Causal Discovery	Oct 15, 2022	Active LearningCausal Discovery	CodeCode Available	0
Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning	Oct 13, 2022	Active LearningInformativeness	CodeCode Available	1
Geometric Active Learning for Segmentation of Large 3D Volumes	Oct 13, 2022	Active LearningSegmentation	—Unverified	0
Data augmentation on-the-fly and active learning in data stream classification	Oct 13, 2022	Active LearningData Augmentation	CodeCode Available	0
Reducing Annotation Effort by Identifying and Labeling Contextually Diverse Classes for Semantic Segmentation Under Domain Shift	Oct 13, 2022	Active LearningDomain Adaptation	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