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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 1461–1470 of 3073 papers

Title	Date	Tasks	Status	Hype
Active learning with MaskAL reduces annotation effort for training Mask R-CNN	Dec 13, 2021	Active Learning	CodeCode Available	1
Depth Uncertainty Networks for Active Learning	Dec 13, 2021	Active Learning	—Unverified	0
Gamifying optimization: a Wasserstein distance-based analysis of human search	Dec 12, 2021	Active LearningDecision Making	—Unverified	0
CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation	Dec 11, 2021	Active LearningSemantic Segmentation	—Unverified	0
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
Multi-View Active Learning for Short Text Classification in User-Generated Data	Dec 5, 2021	Active LearningLanguage Modelling	—Unverified	0
Robust Active Learning: Sample-Efficient Training of Robust Deep Learning Models	Dec 5, 2021	Active Learning	—Unverified	0
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

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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