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 421–430 of 3073 papers

Title	Date	Tasks	Status	Score
A3: Active Adversarial Alignment for Source-Free Domain Adaptation	Sep 27, 2024	Active LearningDomain Adaptation	CodeCode Available	5
Active learning via informed search in movement parameter space for efficient robot task learning and transfer	Feb 21, 2019	Active Learning	CodeCode Available	5
Confidence-Aware Active Feedback for Interactive Instance Search	Oct 23, 2021	Active LearningInstance Search	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
Cost-Sensitive Reference Pair Encoding for Multi-Label Learning	Nov 29, 2016	Active LearningMulti-Label Classification	CodeCode Available	5
Deep Active Alignment of Knowledge Graph Entities and Schemata	Apr 10, 2023	Active LearningDeep Learning	CodeCode Available	5
Detecting value-expressive text posts in Russian social media	Dec 14, 2023	Active LearningSpam detection	CodeCode Available	5
Active Learning for Decision-Making from Imbalanced Observational Data	Apr 10, 2019	Active LearningDecision Making	CodeCode Available	5
Active Learning for Deep Detection Neural Networks	Nov 20, 2019	Active LearningPedestrian Detection	CodeCode Available	5
Combining MixMatch and Active Learning for Better Accuracy with Fewer Labels	Dec 2, 2019	Active Learning	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