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 161–170 of 3073 papers

Title	Date	Tasks	Status	Hype
Active Learning by Feature Mixing	Mar 14, 2022	Active Learning	CodeCode Available	1
Learning Distinctive Margin toward Active Domain Adaptation	Mar 11, 2022	Active LearningDomain Adaptation	CodeCode Available	1
Optical Flow Training under Limited Label Budget via Active Learning	Mar 9, 2022	Active LearningOptical Flow Estimation	CodeCode Available	1
BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotation	Mar 4, 2022	Active Learning	CodeCode Available	1
Information Gain Propagation: a new way to Graph Active Learning with Soft Labels	Mar 2, 2022	Active Learning	CodeCode Available	1
Biological Sequence Design with GFlowNets	Mar 2, 2022	Active LearningDiversity	CodeCode Available	1
Neural Galerkin Schemes with Active Learning for High-Dimensional Evolution Equations	Mar 2, 2022	Active LearningVocal Bursts Intensity Prediction	CodeCode Available	1
FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction	Feb 16, 2022	Active LearningKnowledge Distillation	CodeCode Available	1
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation	Feb 14, 2022	Active Learning	CodeCode Available	1
D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic Segmentation	Feb 14, 2022	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