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 131140 of 3073 papers

TitleStatusHype
A Survey of Dataset Refinement for Problems in Computer Vision DatasetsCode1
Active Learning from the WebCode1
Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active LearningCode1
VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose EstimationCode1
Efficient Bayesian Updates for Deep Learning via Laplace ApproximationsCode1
Making Your First Choice: To Address Cold Start Problem in Vision Active LearningCode1
ActiveNeRF: Learning where to See with Uncertainty EstimationCode1
Active Learning for Optimal Intervention Design in Causal ModelsCode1
BenchPress: A Deep Active Benchmark GeneratorCode1
A Holistic Approach to Undesired Content Detection in the Real WorldCode1
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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