SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1451–1475 of 3073 papers

TitleStatusHype
Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-TrainingCode1
Unsupervised Clustering Active Learning for Person Re-identification—0
On the relationship between calibrated predictors and unbiased volume estimationCode0
Fair Active Learning: Solving the Labeling Problem in Insurance—0
AcTune: Uncertainty-aware Active Self-Training for Semi-Supervised Active Learning with Pretrained Language ModelsCode1
Curriculum learning for data-driven modeling of dynamical systems—0
Assisted Text Annotation Using Active Learning to Achieve High Quality with Little Effort—0
Towards General and Efficient Active LearningCode1
LMTurk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Framework—0
Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not—0
Active learning with MaskAL reduces annotation effort for training Mask R-CNNCode1
Depth Uncertainty Networks for Active Learning—0
Gamifying optimization: a Wasserstein distance-based analysis of human search—0
CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation—0
Boosting Active Learning via Improving Test PerformanceCode1
Active Sensing for Communications by LearningCode1
Multi-View Active Learning for Short Text Classification in User-Generated Data—0
Robust Active Learning: Sample-Efficient Training of Robust Deep Learning Models—0
A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled SamplesCode1
Active Learning for Domain Adaptation: An Energy-Based ApproachCode1
SEAL: Self-supervised Embodied Active Learning using Exploration and 3D Consistency—0
A Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning—0
Online Active Learning with Surrogate Loss Functions—0
Learning with Labeling Induced Abstentions—0
Active Learning of Convex Halfspaces on Graphs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified