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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 221–230 of 3073 papers

Title	Date	Tasks	Status	Hype
Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic Segmentation	Aug 22, 2023	Active LearningPoint Cloud Segmentation	CodeCode Available	1
Hitting the Target: Stopping Active Learning at the Cost-Based Optimum	Oct 7, 2021	Active Learning	CodeCode Available	1
Active learning with MaskAL reduces annotation effort for training Mask R-CNN	Dec 13, 2021	Active Learning	CodeCode Available	1
Active Learning from the Web	Oct 15, 2022	Active LearningInformation Retrieval	CodeCode Available	1
Active Learning for Optimal Intervention Design in Causal Models	Sep 10, 2022	Active LearningExperimental Design	CodeCode Available	1
infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information	May 30, 2023	Active LearningInformativeness	CodeCode Available	1
Is segmentation uncertainty useful?	Mar 30, 2021	Active LearningImage Segmentation	CodeCode Available	1
Iterative Loop Method Combining Active and Semi-Supervised Learning for Domain Adaptive Semantic Segmentation	Jan 31, 2023	Active LearningDomain Adaptation	CodeCode Available	1
Active Learning for Domain Adaptation: An Energy-Based Approach	Dec 2, 2021	Active LearningDomain Adaptation	CodeCode Available	1
Active Learning Helps Pretrained Models Learn the Intended Task	Apr 18, 2022	Active Learning	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