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 1631–1640 of 3073 papers

TitleStatusHype
Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection—0
Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection—0
Representative Subset Selection for Efficient Fine-Tuning in Self-Supervised Speech Recognition—0
Towards Unconstrained 2D Pose Estimation of the Human Spine—0
Toward Supervised Anomaly Detection—0
Towards Visual Explainable Active Learning for Zero-Shot Classification—0
Training Data Distribution Search with Ensemble Active Learning—0
Transferable Query Selection for Active Domain Adaptation—0
Transfer Active Learning For Graph Neural Networks—0
TrustAL: Trustworthy Active Learning using Knowledge Distillation—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