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 141–150 of 3073 papers

Title	Date	Tasks	Status	Hype
CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume Segmentation	Aug 2, 2022	Active LearningSegmentation	CodeCode Available	1
Active Learning Strategies for Weakly-supervised Object Detection	Jul 25, 2022	Active LearningObject	CodeCode Available	1
Active Pointly-Supervised Instance Segmentation	Jul 23, 2022	Active LearningInstance Segmentation	CodeCode Available	1
Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentation	Jul 2, 2022	Active LearningBenchmarking	CodeCode Available	1
AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language Models	Jul 1, 2022	Active Learningtext-classification	CodeCode Available	1
Deep Active Learning for Regression Using ε-weighted Hybrid Query Strategy	Jun 24, 2022	Active Learningregression	CodeCode Available	1
ICS: Total Freedom in Manual Text Classification Supported by Unobtrusive Machine Learning	Jun 22, 2022	Active LearningClassification	CodeCode Available	1
Active Bayesian Causal Inference	Jun 4, 2022	Active LearningCausal Discovery	CodeCode Available	1
PyRelationAL: a python library for active learning research and development	May 23, 2022	Active LearningBenchmarking	CodeCode Available	1
Active Learning Through a Covering Lens	May 23, 2022	Active LearningRepresentation Learning	CodeCode Available	1

Show:10 25 50

← PrevPage 15 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