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 111–120 of 3073 papers

Title	Date	Tasks	Status	Hype	Score
Active Transfer Learning for Efficient Video-Specific Human Pose Estimation	Nov 8, 2023	Active LearningPose Estimation	CodeCode Available	1	5
Building a Scalable and Interpretable Bayesian Deep Learning Framework for Quality Control of Free Form Surfaces	Apr 7, 1994	Active LearningBenchmarking	CodeCode Available	1	5
Cartography Active Learning	Sep 9, 2021	Active Learningtext-classification	CodeCode Available	1	5
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data	Nov 3, 2021	Active Learning	CodeCode Available	1	5
AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language Models	Jul 1, 2022	Active Learningtext-classification	CodeCode Available	1	5
Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-Training	Dec 27, 2021	3D Pose EstimationActive Learning	CodeCode Available	1	5
CitySurfaces: City-Scale Semantic Segmentation of Sidewalk Materials	Jan 6, 2022	Active LearningManagement	CodeCode Available	1	5
Active learning with MaskAL reduces annotation effort for training Mask R-CNN	Dec 13, 2021	Active Learning	CodeCode Available	1	5
A Benchmark on Uncertainty Quantification for Deep Learning Prognostics	Feb 9, 2023	Active LearningDecision Making	CodeCode Available	1	5
Active Anomaly Detection via Ensembles	Sep 17, 2018	Active LearningAnomaly Detection	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