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 25412550 of 3073 papers

TitleStatusHype
How useful is Active Learning for Image-based Plant Phenotyping?Code0
Automated Progressive Red TeamingCode0
Data augmentation on-the-fly and active learning in data stream classificationCode0
Context Selection and Rewriting for Video-based Educational Question GenerationCode0
Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic QuantitiesCode0
Test-time augmentation-based active learning and self-training for label-efficient segmentationCode0
Constraining the Parameters of High-Dimensional Models with Active LearningCode0
Adversarial Distillation of Bayesian Neural Network PosteriorsCode0
Towards Efficient Active Learning of PDFACode0
Unique Rashomon Sets for Robust 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