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 121–130 of 3073 papers

Title	Date	Tasks	Status	Hype	Score
ActiveNeRF: Learning where to See with Uncertainty Estimation	Sep 18, 2022	Active LearningNeRF	CodeCode Available	1	5
Active Anomaly Detection via Ensembles	Sep 17, 2018	Active LearningAnomaly Detection	CodeCode Available	1	5
Active Prompt Learning in Vision Language Models	Nov 18, 2023	Active LearningPrompt 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
Adaptive Superpixel for Active Learning in Semantic Segmentation	Mar 29, 2023	Active LearningSegmentation	CodeCode Available	1	5
CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization	Jul 8, 2025	Active LearningAutomated Theorem Proving	CodeCode Available	1	5
Active Learning for Bayesian 3D Hand Pose Estimation	Oct 1, 2020	3D Hand Pose EstimationActive Learning	CodeCode Available	1	5
Accelerating high-throughput virtual screening through molecular pool-based active learning	Dec 13, 2020	Active LearningBayesian Optimization	CodeCode Available	1	5
Active Learning for Coreference Resolution using Discrete Annotation	Apr 28, 2020	Active LearningClustering	CodeCode Available	1	5
D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic Segmentation	Feb 14, 2022	Active LearningDomain Adaptation	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