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 2951–2960 of 3073 papers

Title	Date	Tasks	Status
Reducing Aleatoric and Epistemic Uncertainty through Multi-modal Data Acquisition	Jan 30, 2025	Active LearningDisentanglement	CodeCode Available
Finding the Homology of Decision Boundaries with Active Learning	Nov 19, 2020	Active LearningMeta-Learning	CodeCode Available
Find Rhinos without Finding Rhinos: Active Learning with Multimodal Imagery of South African Rhino Habitats	Sep 26, 2024	Active Learning	CodeCode Available
Learning to Caption Images through a Lifetime by Asking Questions	Dec 1, 2018	Active LearningImage Captioning	CodeCode Available
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization	May 28, 2018	Active LearningContinuous Object Recognition	CodeCode Available
Reducing Annotating Load: Active Learning with Synthetic Images in Surgical Instrument Segmentation	Aug 7, 2021	Active Learning	CodeCode Available
Reducing Annotation Effort by Identifying and Labeling Contextually Diverse Classes for Semantic Segmentation Under Domain Shift	Oct 13, 2022	Active LearningDomain Adaptation	CodeCode Available
A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT Images	Jul 7, 2020	Active Learning	CodeCode Available
A Variance Maximization Criterion for Active Learning	Jun 23, 2017	Active LearningInformativeness	CodeCode Available
Active Learning for Argument Strength Estimation	Sep 23, 2021	Active LearningArgument Mining	CodeCode Available

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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