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 2371–2380 of 3073 papers

TitleStatusHype
Deep Active Learning for Anchor User PredictionCode0
RadGrad: Active learning with loss gradients—0
A sparse annotation strategy based on attention-guided active learning for 3D medical image segmentation—0
Low-resource Deep Entity Resolution with Transfer and Active Learning—0
Bounded Expectation of Label Assignment: Dataset Annotation by Supervised Splitting with Bias-Reduction Techniques—0
Active Generative Adversarial Network for Image Classification—0
Online Active Learning of Reject Option Classifiers—0
Non-Parametric Calibration for ClassificationCode0
Evaluation of Seed Set Selection Approaches and Active Learning Strategies in Predictive Coding—0
Human-Machine Collaboration for Fast Land Cover Mapping—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