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 801–810 of 3073 papers

Title	Date	Tasks	Status
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
Active Learning for Video Classification with Frame Level Queries	Jul 10, 2023	Active LearningClassification	—Unverified
DADO -- Low-Cost Query Strategies for Deep Active Design Optimization	Jul 10, 2023	Active Learning	—Unverified
Active Learning in Physics: From 101, to Progress, and Perspective	Jul 8, 2023	Active Learning	—Unverified
For Women, Life, Freedom: A Participatory AI-Based Social Web Analysis of a Watershed Moment in Iran's Gender Struggles	Jul 7, 2023	Active Learning	—Unverified
Training Ensembles with Inliers and Outliers for Semi-supervised Active Learning	Jul 7, 2023	Active LearningOutlier Detection	CodeCode Available
Active Learning with Contrastive Pre-training for Facial Expression Recognition	Jul 6, 2023	Active LearningFacial Expression Recognition	CodeCode Available
Understanding Uncertainty Sampling	Jul 6, 2023	Active Learning	CodeCode Available
Optimal and Efficient Binary Questioning for Human-in-the-Loop Annotation	Jul 4, 2023	Active LearningBinary Classification	—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