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 751760 of 3073 papers

TitleStatusHype
Few-shot Named Entity Recognition via Superposition Concept DiscriminationCode0
Enhancing Semi-supervised Domain Adaptation via Effective Target LabelingCode0
On the Fragility of Active Learners for Text ClassificationCode0
An active learning model to classify animal species in Hong Kong0
Generative Active Learning for Image Synthesis PersonalizationCode0
CODA: A COst-efficient Test-time Domain Adaptation Mechanism for HAR0
Modular Deep Active Learning Framework for Image Annotation: A Technical Report for the Ophthalmo-AI Project0
DP-Dueling: Learning from Preference Feedback without Compromising User Privacy0
Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networks0
Deep Active Learning: A Reality Check0
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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