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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 81–90 of 3073 papers

Title	Date	Tasks	Status	Hype
Active learning for medical image segmentation with stochastic batches	Jan 18, 2023	Active LearningDiversity	CodeCode Available	1
Active learning with MaskAL reduces annotation effort for training Mask R-CNN	Dec 13, 2021	Active Learning	CodeCode Available	1
Active Learning Helps Pretrained Models Learn the Intended Task	Apr 18, 2022	Active Learning	CodeCode Available	1
Active Pointly-Supervised Instance Segmentation	Jul 23, 2022	Active LearningInstance Segmentation	CodeCode Available	1
Active Prompt Learning in Vision Language Models	Nov 18, 2023	Active LearningPrompt Learning	CodeCode Available	1
Active Anomaly Detection via Ensembles	Sep 17, 2018	Active LearningAnomaly Detection	CodeCode Available	1
Active Learning for Deep Object Detection via Probabilistic Modeling	Mar 30, 2021	Active LearningClassification	CodeCode Available	1
Active Testing: Sample-Efficient Model Evaluation	Mar 9, 2021	Active LearningGaussian Processes	CodeCode Available	1
A Comparative Survey of Deep Active Learning	Mar 25, 2022	Active LearningSurvey	CodeCode Available	1
Active Learning for Domain Adaptation: An Energy-Based Approach	Dec 2, 2021	Active LearningDomain Adaptation	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