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 781–790 of 3073 papers

Title	Date	Tasks	Status	Hype
Hybrid Representation-Enhanced Sampling for Bayesian Active Learning in Musculoskeletal Segmentation of Lower Extremities	Jul 26, 2023	Active LearningDiversity	CodeCode Available	0
Robust Assignment of Labels for Active Learning with Sparse and Noisy Annotations	Jul 25, 2023	Active Learning	—Unverified	0
Geometry-Aware Adaptation for Pretrained Models	Jul 23, 2023	Active Learning	—Unverified	0
Efficient Gaussian Process Classification-based Physical-Layer Authentication with Configurable Fingerprints for 6G-Enabled IoT	Jul 23, 2023	Active LearningGaussian Processes	—Unverified	0
COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentation	Jul 22, 2023	Active LearningImage Segmentation	CodeCode Available	1
Divide and Adapt: Active Domain Adaptation via Customized Learning	Jul 21, 2023	Active LearningDomain Adaptation	CodeCode Available	1
EdgeAL: An Edge Estimation Based Active Learning Approach for OCT Segmentation	Jul 20, 2023	Active LearningSuperpixels	CodeCode Available	0
Clinical Trial Active Learning	Jul 20, 2023	Active Learning	CodeCode Available	0
Novel Batch Active Learning Approach and Its Application to Synthetic Aperture Radar Datasets	Jul 19, 2023	Active LearningGraph Learning	CodeCode Available	0
Confidence Estimation Using Unlabeled Data	Jul 19, 2023	Active Learningimage-classification	CodeCode Available	0

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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