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 1041–1050 of 3073 papers

Title	Date	Tasks	Status
Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning	Jul 17, 2023	3D Object DetectionActive Learning	—Unverified
Active Learning for Object Detection with Non-Redundant Informative Sampling	Jul 17, 2023	Active LearningDiversity	—Unverified
KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection	Jul 16, 2023	3D Object DetectionActive Learning	—Unverified
Recognition of Mental Adjectives in An Efficient and Automatic Style	Jul 16, 2023	Active LearningSentiment Analysis	—Unverified
Exploiting Counter-Examples for Active Learning with Partial labels	Jul 14, 2023	Active Learning	CodeCode Available
Defect Classification in Additive Manufacturing Using CNN-Based Vision Processing	Jul 14, 2023	Active Learning	—Unverified
Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation	Jul 14, 2023	Active LearningInformativeness	CodeCode Available
Unsupervised Learning of Distributional Properties can Supplement Human Labeling and Increase Active Learning Efficiency in Anomaly Detection	Jul 13, 2023	Active LearningAnomaly Detection	—Unverified
OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image Classification	Jul 11, 2023	Active Learningimage-classification	CodeCode Available
DADO -- Low-Cost Query Strategies for Deep Active Design Optimization	Jul 10, 2023	Active Learning	—Unverified

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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