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 361–370 of 3073 papers

Title	Date	Tasks	Status	Score
Continual egocentric object recognition	Dec 6, 2019	Active LearningNovelty Detection	CodeCode Available	5
Continual Deep Active Learning for Medical Imaging: Replay-Base Architecture for Context Adaptation	Jan 14, 2025	Active Learningclass-incremental learning	CodeCode Available	5
ABC3: Active Bayesian Causal Inference with Cohn Criteria in Randomized Experiments	Dec 15, 2024	Active LearningCausal Inference	CodeCode Available	5
Active Learning for Deep Gaussian Process Surrogates	Dec 15, 2020	Active LearningGaussian Processes	CodeCode Available	5
Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics	Nov 25, 2021	Active LearningAge Estimation	CodeCode Available	5
Controllable Textual Inversion for Personalized Text-to-Image Generation	Apr 11, 2023	Active LearningImage Generation	CodeCode Available	5
Cost-Effective Active Learning for Deep Image Classification	Jan 13, 2017	Active LearningClassification	CodeCode Available	5
Active Learning for Deep Detection Neural Networks	Nov 20, 2019	Active LearningPedestrian Detection	CodeCode Available	5
Active Learning for Decision-Making from Imbalanced Observational Data	Apr 10, 2019	Active LearningDecision Making	CodeCode Available	5
Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation	Nov 6, 2024	Active LearningBayesian Optimization	CodeCode Available	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