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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 101–110 of 3073 papers

Title	Date	Tasks	Status	Hype
A comprehensive survey on deep active learning in medical image analysis	Oct 22, 2023	Active LearningInformativeness	CodeCode Available	1
A Simple Baseline for Low-Budget Active Learning	Oct 22, 2021	Active Learningimage-classification	CodeCode Available	1
Active learning for medical image segmentation with stochastic batches	Jan 18, 2023	Active LearningDiversity	CodeCode Available	1
A Survey of Dataset Refinement for Problems in Computer Vision Datasets	Oct 21, 2022	Active Learning	CodeCode Available	1
A Tutorial on Thompson Sampling	Jul 7, 2017	Active LearningProduct Recommendation	CodeCode Available	1
Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings	Oct 16, 2020	Active LearningClustering	CodeCode Available	1
Bayesian active learning for production, a systematic study and a reusable library	Jun 17, 2020	Active Learning	CodeCode Available	1
Bayesian Active Learning with Fully Bayesian Gaussian Processes	May 20, 2022	Active LearningGaussian Processes	CodeCode Available	1
Bayesian Model-Agnostic Meta-Learning	Jun 11, 2018	Active Learningimage-classification	CodeCode Available	1
Active Learning for Open-set Annotation	Jan 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