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 2821–2830 of 3073 papers

Title	Date	Tasks	Status
ViewAL: Active Learning with Viewpoint Entropy for Semantic Segmentation	Nov 26, 2019	Active LearningSemantic Segmentation	CodeCode Available
Large-Scale Dataset Pruning in Adversarial Training through Data Importance Extrapolation	Jun 19, 2024	Active LearningDeep Learning	CodeCode Available
VirAAL: Virtual Adversarial Active Learning For NLU	May 14, 2020	Active LearningData Augmentation	CodeCode Available
On Bayesian Search for the Feasible Space Under Computationally Expensive Constraints	Apr 23, 2020	Active LearningGaussian Processes	CodeCode Available
Enhancing Retinal Disease Classification from OCTA Images via Active Learning Techniques	Jul 21, 2024	Active Learning	CodeCode Available
Onception: Active Learning with Expert Advice for Real World Machine Translation	Mar 9, 2022	Active LearningMachine Translation	CodeCode Available
Enhancing Semi-supervised Domain Adaptation via Effective Target Labeling	Mar 24, 2024	Active LearningDomain Adaptation	CodeCode Available
Enhancing Semi-Supervised Learning via Representative and Diverse Sample Selection	Sep 18, 2024	Active Learning	CodeCode Available
Enhancing Text Classification through LLM-Driven Active Learning and Human Annotation	Jun 17, 2024	Active LearningSentiment Analysis	CodeCode Available
Active Preference Optimization for Sample Efficient RLHF	Feb 16, 2024	Active Learning	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