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 931–940 of 3073 papers

Title	Date	Tasks	Status	Hype
Box-Level Active Detection	Mar 23, 2023	Active Learningobject-detection	CodeCode Available	1
Uncertainty Aware Active Learning for Reconfiguration of Pre-trained Deep Object-Detection Networks for New Target Domains	Mar 22, 2023	Active LearningInformativeness	—Unverified	0
Re-thinking Federated Active Learning based on Inter-class Diversity	Mar 22, 2023	Active LearningDiversity	CodeCode Available	1
Active Coarse-to-Fine Segmentation of Moveable Parts from Real Images	Mar 21, 2023	Active LearningDiversity	—Unverified	0
Agave crop segmentation and maturity classification with deep learning data-centric strategies using very high-resolution satellite imagery	Mar 21, 2023	Active LearningData Augmentation	—Unverified	0
A general-purpose AI assistant embedded in an open-source radiology information system	Mar 18, 2023	Active LearningFew-Shot Learning	—Unverified	0
Active Learning-based Model Predictive Coverage Control	Mar 17, 2023	Active Learningmodel	—Unverified	0
Stochastic Submodular Maximization via Polynomial Estimators	Mar 17, 2023	Active Learning	—Unverified	0
Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systems	Mar 17, 2023	Active LearningGaussian Processes	CodeCode Available	0
LRDB: LSTM Raw data DNA Base-caller based on long-short term models in an active learning environment	Mar 15, 2023	Active LearningTransfer Learning	—Unverified	0

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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