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 91–100 of 3073 papers

Title	Date	Tasks	Status	Hype
A2-LINK: Recognizing Disguised Faces via Active Learning and Adversarial Noise based Inter-Domain Knowledge	Jun 1, 2020	Active LearningDomain Adaptation	CodeCode Available	1
Adaptive Superpixel for Active Learning in Semantic Segmentation	Mar 29, 2023	Active LearningSegmentation	CodeCode Available	1
A deep active learning system for species identification and counting in camera trap images	Oct 22, 2019	Active LearningDecision Making	CodeCode Available	1
Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center Dataset	Jun 19, 2024	Active LearningDomain Adaptation	CodeCode Available	1
Active Learning for Deep Object Detection via Probabilistic Modeling	Mar 30, 2021	Active LearningClassification	CodeCode Available	1
AISecKG: Knowledge Graph Dataset for Cybersecurity Education	Mar 30, 2023	Active LearningKnowledge Graphs	CodeCode Available	1
AL-GTD: Deep Active Learning for Gaze Target Detection	Sep 27, 2024	Active Learning	CodeCode Available	1
A-LINK: Recognizing Disguised Faces via Active Learning based Inter-Domain Knowledge	Sep 23, 2019	Active LearningDomain Adaptation	CodeCode Available	1
A Mathematical Analysis of Learning Loss for Active Learning in Regression	Apr 19, 2021	Active LearningPose Estimation	CodeCode Available	1
Active Learning for Domain Adaptation: An Energy-Based Approach	Dec 2, 2021	Active LearningDomain Adaptation	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