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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 251–260 of 3073 papers

Title	Date	Tasks	Status	Hype
Active Learning Helps Pretrained Models Learn the Intended Task	Apr 18, 2022	Active Learning	CodeCode Available	1
Parallelized Acquisition for Active Learning using Monte Carlo Sampling	May 30, 2023	Active LearningBayesian Inference	CodeCode Available	1
Learning Loss for Active Learning	May 9, 2019	Active Learningimage-classification	CodeCode Available	1
Post-hoc Probabilistic Vision-Language Models	Dec 8, 2024	Active LearningUncertainty Quantification	CodeCode Available	1
Active Learning Meets Optimized Item Selection	Nov 22, 2021	Active LearningClustering	CodeCode Available	1
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite Images	Oct 15, 2021	Active LearningLand Cover Classification	CodeCode Available	1
ActiveGLAE: A Benchmark for Deep Active Learning with Transformers	Jun 16, 2023	Active Learning	CodeCode Available	1
Active Learning from the Web	Oct 15, 2022	Active LearningInformation Retrieval	CodeCode Available	1
Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)	Oct 6, 2021	Active Learning	CodeCode Available	1
Active learning with MaskAL reduces annotation effort for training Mask R-CNN	Dec 13, 2021	Active Learning	CodeCode Available	1

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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