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 41–50 of 3073 papers

Title	Date	Tasks	Status	Hype
Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-Training	Dec 27, 2021	3D Pose EstimationActive Learning	CodeCode Available	1
A2-LINK: Recognizing Disguised Faces via Active Learning and Adversarial Noise based Inter-Domain Knowledge	Jun 1, 2020	Active LearningDomain Adaptation	CodeCode Available	1
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite Images	Oct 15, 2021	Active LearningLand Cover Classification	CodeCode Available	1
A Comparative Survey of Deep Active Learning	Mar 25, 2022	Active LearningSurvey	CodeCode Available	1
Active Learning for Deep Object Detection via Probabilistic Modeling	Mar 30, 2021	Active LearningClassification	CodeCode Available	1
Active Learning for Domain Adaptation: An Energy-Based Approach	Dec 2, 2021	Active LearningDomain Adaptation	CodeCode Available	1
Active learning for medical image segmentation with stochastic batches	Jan 18, 2023	Active LearningDiversity	CodeCode Available	1
Active Learning Helps Pretrained Models Learn the Intended Task	Apr 18, 2022	Active Learning	CodeCode Available	1
Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)	Oct 6, 2021	Active Learning	CodeCode Available	1
Active Learning for Computationally Efficient Distribution of Binary Evolution Simulations	Mar 30, 2022	Active Learningregression	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