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 2321–2330 of 3073 papers

TitleStatusHype
An Adaptive Supervision Framework for Active Learning in Object Detection—0
Bayesian Batch Active Learning as Sparse Subset ApproximationCode0
Automatic Playtesting for Game Parameter Tuning via Active Learning—0
A Survey on Deep Learning of Small Sample in Biomedical Image AnalysisCode0
Incremental Domain Adaptation for Neural Machine Translation in Low-Resource SettingsCode0
FDive: Learning Relevance Models using Pattern-based Similarity Measures—0
Mindful Active LearningCode0
Automated Muscle Segmentation from Clinical CT using Bayesian U-Net for Personalized Musculoskeletal ModelingCode0
Photonic architecture for reinforcement learning—0
Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning—0
Show:102550
← PrevPage 233 of 308Next →

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