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 271–280 of 3073 papers

Title	Date	Tasks	Status	Hype
Deep Active Learning in Remote Sensing for data efficient Change Detection	Aug 25, 2020	Active LearningChange Detection	CodeCode Available	1
Contextual Diversity for Active Learning	Aug 13, 2020	Active LearningDiversity	CodeCode Available	1
DeepDrummer : Generating Drum Loops using Deep Learning and a Human in the Loop	Aug 10, 2020	Active LearningEfficient Exploration	CodeCode Available	1
DEAL: Deep Evidential Active Learning for Image Classification	Jul 22, 2020	Active LearningClassification	CodeCode Available	1
On uncertainty estimation in active learning for image segmentation	Jul 13, 2020	Active LearningImage Segmentation	CodeCode Available	1
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction	Jul 7, 2020	Active LearningGraph Neural Network	CodeCode Available	1
Confidence-Aware Learning for Deep Neural Networks	Jul 3, 2020	Active LearningOut-of-Distribution Detection	CodeCode Available	1
CheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic Output	Jun 26, 2020	Active LearningDiagnostic	CodeCode Available	1
Graph Policy Network for Transferable Active Learning on Graphs	Jun 24, 2020	Active Learning	CodeCode Available	1
Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty Quantification	Jun 22, 2020	Active LearningExperimental Design	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