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

Showing 201210 of 3073 papers

TitleStatusHype
Evidential Uncertainty Quantification: A Variance-Based PerspectiveCode1
Active Learning for Computationally Efficient Distribution of Binary Evolution SimulationsCode1
A comprehensive survey on deep active learning in medical image analysisCode1
AL-GTD: Deep Active Learning for Gaze Target DetectionCode1
A-LINK: Recognizing Disguised Faces via Active Learning based Inter-Domain KnowledgeCode1
FAMIE: A Fast Active Learning Framework for Multilingual Information ExtractionCode1
Active Learning for Convolutional Neural Networks: A Core-Set ApproachCode1
Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image ClassificationCode1
A Mathematical Analysis of Learning Loss for Active Learning in RegressionCode1
A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled SamplesCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified