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 141–150 of 3073 papers

Title	Date	Tasks	Status	Hype	Score
Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts	Feb 3, 2018	Active LearningTransfer Learning	CodeCode Available	1	5
All you need are a few pixels: semantic segmentation with PixelPick	Apr 13, 2021	Active LearningAll	CodeCode Available	1	5
Accelerating high-throughput virtual screening through molecular pool-based active learning	Dec 13, 2020	Active LearningBayesian Optimization	CodeCode Available	1	5
A Mathematical Analysis of Learning Loss for Active Learning in Regression	Apr 19, 2021	Active LearningPose Estimation	CodeCode Available	1	5
An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping	Feb 7, 2023	Active LearningSemantic Segmentation	CodeCode Available	1	5
Are Binary Annotations Sufficient? Video Moment Retrieval via Hierarchical Uncertainty-Based Active Learning	Jan 1, 2023	Active LearningMoment Retrieval	CodeCode Available	1	5
A Simple Baseline for Low-Budget Active Learning	Oct 22, 2021	Active Learningimage-classification	CodeCode Available	1	5
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite Images	Oct 15, 2021	Active LearningLand Cover Classification	CodeCode Available	1	5
Active Anomaly Detection via Ensembles	Sep 17, 2018	Active LearningAnomaly Detection	CodeCode Available	1	5
A deep active learning system for species identification and counting in camera trap images	Oct 22, 2019	Active LearningDecision Making	CodeCode Available	1	5

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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