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 1891–1900 of 3073 papers

Title	Date	Tasks	Status
Semantic Segmentation with Active Semi-Supervised Learning	Mar 21, 2022	Active LearningSegmentation	—Unverified
Semantic Segmentation with Active Semi-Supervised Representation Learning	Oct 16, 2022	Active LearningContrastive Learning	—Unverified
Semantics for Large-Scale Multimedia: New Challenges for NLP	Jun 1, 2014	Active LearningInformation Retrieval	—Unverified
Semi-automated Annotation of Signal Events in Clinical EEG Data	Jan 3, 2018	Active LearningBIG-bench Machine Learning	—Unverified
Semi-Supervised Active Learning for COVID-19 Lung Ultrasound Multi-symptom Classification	Sep 9, 2020	Active LearningDiagnostic	—Unverified
Semi-supervised Active Learning for Instance Segmentation via Scoring Predictions	Dec 9, 2020	Active Learningimage-classification	—Unverified
Semi-Supervised Active Learning for Semi-Supervised Models: Exploit Adversarial Examples With Graph-Based Virtual Labels	Jan 1, 2021	Active Learning	—Unverified
Semi-Supervised Active Learning for Support Vector Machines: A Novel Approach that Exploits Structure Information in Data	Oct 13, 2016	Active LearningGeneral Classification	—Unverified
Semi-supervised Active Regression	Jun 12, 2021	Active Learningregression	—Unverified
SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs	Aug 22, 2019	Active LearningGraph Embedding	—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