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 26912700 of 3073 papers

TitleStatusHype
Information-Theoretic Active Learning for Content-Based Image RetrievalCode0
Discovering General-Purpose Active Learning StrategiesCode0
Approximate Bayesian Computation with Domain Expert in the LoopCode0
Sampling Bias in Deep Active Classification: An Empirical StudyCode0
Discovery of Self-Assembling π-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular SimulationCode0
A-Optimal Active LearningCode0
Bidirectional Uncertainty-Based Active Learning for Open Set AnnotationCode0
Predictive Accuracy-Based Active Learning for Medical Image SegmentationCode0
Discriminative Active LearningCode0
Instance-wise Supervision-level Optimization in Active LearningCode0
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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