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 20112020 of 3073 papers

TitleStatusHype
The CAMOMILE Collaborative Annotation Platform for Multi-modal, Multi-lingual and Multi-media Documents0
The Cost of Replicability in Active Learning0
The Effectiveness of Variational Autoencoders for Active Learning0
Bayesian Active Learning in the Presence of Nuisance Parameters0
The Impact of Typicality for Informative Representative Selection0
The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning0
The Infinite Index: Information Retrieval on Generative Text-To-Image Models0
The Power of Comparisons for Actively Learning Linear Classifiers0
The Power of Ensembles for Active Learning in Image Classification0
The Power of Localization for Efficiently Learning Linear Separators with Noise0
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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