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 241250 of 3073 papers

TitleStatusHype
MoBYv2AL: Self-supervised Active Learning for Image ClassificationCode1
Model Assertions for Monitoring and Improving ML ModelsCode1
Active Learning from the WebCode1
Active Learning Meets Optimized Item SelectionCode1
Multi-Objective GFlowNetsCode1
Multiple instance active learning for object detectionCode1
Learning Loss for Active LearningCode1
Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance AssessmentCode1
ActiveGLAE: A Benchmark for Deep Active Learning with TransformersCode1
Active learning for medical image segmentation with stochastic batchesCode1
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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