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 731–740 of 3073 papers

TitleStatusHype
Active Learning for Fine-Grained Sketch-Based Image Retrieval—0
ALWOD: Active Learning for Weakly-Supervised Object DetectionCode0
Semantic Parsing in Limited Resource Conditions—0
Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random SelectionCode0
Active Label Refinement for Semantic Segmentation of Satellite Images—0
ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular GenerationCode1
LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus ImagesCode0
Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss—0
Learning Objective-Specific Active Learning Strategies with Attentive Neural ProcessesCode0
Active Learning for Classifying 2D Grid-Based Level CompletabilityCode0
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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