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 2561–2570 of 3073 papers

TitleStatusHype
Active search for Bifurcations—0
Active Self-Paced Learning for Cost-Effective and Progressive Face Identification—0
Active Self-Semi-Supervised Learning for Few Labeled Samples—0
Active Self-Supervised Learning: A Few Low-Cost Relationships Are All You Need—0
Active Self-Training for Weakly Supervised 3D Scene Semantic Segmentation—0
Active Semi-Supervised Learning by Exploring Per-Sample Uncertainty and Consistency—0
Active Semi-supervised Transfer Learning (ASTL) for Offline BCI Calibration—0
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space—0
Active Sentiment Domain Adaptation—0
ActiveSSF: An Active-Learning-Guided Self-Supervised Framework for Long-Tailed Megakaryocyte Classification—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