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 1481–1490 of 3073 papers

Title	Date	Tasks	Status	Hype
Active Learning at the ImageNet Scale	Nov 25, 2021	Active Learning	CodeCode Available	1
Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation	Nov 25, 2021	Active LearningPrediction	CodeCode Available	1
Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics	Nov 25, 2021	Active LearningAge Estimation	CodeCode Available	0
Active Learning Meets Optimized Item Selection	Nov 22, 2021	Active LearningClustering	CodeCode Available	1
Self-supervised Semi-supervised Learning for Data Labeling and Quality Evaluation	Nov 22, 2021	Active LearningRepresentation Learning	—Unverified	0
Fink: early supernovae Ia classification using active learning	Nov 22, 2021	Active LearningClassification	CodeCode Available	1
YMIR: A Rapid Data-centric Development Platform for Vision Applications	Nov 19, 2021	Active Learning	CodeCode Available	1
Guiding Generative Language Models for Data Augmentation in Few-Shot Text Classification	Nov 17, 2021	Active LearningClassification	—Unverified	0
GFlowNet Foundations	Nov 17, 2021	Active Learning	CodeCode Available	1
Probing Difficulty and Discrimination of Natural Language Questions With Item Response Theory	Nov 16, 2021	Active LearningQuestion Answering	—Unverified	0

Show:10 25 50

← PrevPage 149 of 308Next →

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