SOTAVerified|Agents Browse Leaderboard About

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 261–270 of 3073 papers

Title	Date	Tasks	Status	Hype
Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)	Oct 6, 2021	Active Learning	CodeCode Available	1
Active learning with MaskAL reduces annotation effort for training Mask R-CNN	Dec 13, 2021	Active Learning	CodeCode Available	1
Active Pointly-Supervised Instance Segmentation	Jul 23, 2022	Active LearningInstance Segmentation	CodeCode Available	1
ActiveNeRF: Learning where to See with Uncertainty Estimation	Sep 18, 2022	Active LearningNeRF	CodeCode Available	1
Adaptive Superpixel for Active Learning in Semantic Segmentation	Mar 29, 2023	Active LearningSegmentation	CodeCode Available	1
Active learning for medical image segmentation with stochastic batches	Jan 18, 2023	Active LearningDiversity	CodeCode Available	1
Active Imitation Learning with Noisy Guidance	May 26, 2020	Active LearningImitation Learning	CodeCode Available	1
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation	Feb 14, 2022	Active Learning	CodeCode Available	1
Active Test-Time Adaptation: Theoretical Analyses and An Algorithm	Apr 7, 2024	Active LearningDomain Adaptation	CodeCode Available	1
All you need are a few pixels: semantic segmentation with PixelPick	Apr 13, 2021	Active LearningAll	CodeCode Available	1

Show:10 25 50

← PrevPage 27 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