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 2401–2410 of 3073 papers

Title	Date	Tasks	Status
Active Adversarial Domain Adaptation	Apr 16, 2019	Active LearningDiversity	—Unverified
Exploring Representativeness and Informativeness for Active Learning	Apr 14, 2019	Active LearningInformativeness	—Unverified
Robust and Discriminative Labeling for Multi-label Active Learning Based on Maximum Correntropy Criterion	Apr 14, 2019	Active LearningMulti-Label Learning	—Unverified
Detecting Repeating Objects using Patch Correlation Analysis	Apr 11, 2019	Active LearningObject	—Unverified
BAOD: Budget-Aware Object Detection	Apr 10, 2019	Active LearningObject	—Unverified
Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification	Apr 10, 2019	Active LearningClassification	—Unverified
Active Learning for Decision-Making from Imbalanced Observational Data	Apr 10, 2019	Active LearningDecision Making	CodeCode Available
Context-Aware Query Selection for Active Learning in Event Recognition	Apr 9, 2019	Active LearningActivity Recognition	—Unverified
Generalized active learning and design of statistical experiments for manifold-valued data	Apr 8, 2019	Active LearningBRDF estimation	—Unverified
Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification	Apr 4, 2019	Active LearningGeneral Classification	—Unverified

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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