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 28912900 of 3073 papers

TitleStatusHype
Active Diffusion and VCA-Assisted Image Segmentation of Hyperspectral ImagesCode0
Active Learning with Weak Supervision for Gaussian ProcessesCode0
Vis-DSS: An Open-Source toolkit for Visual Data Selection and SummarizationCode0
RareGAN: Generating Samples for Rare ClassesCode0
Active Learning for Non-Parametric Regression Using Purely Random TreesCode0
TiDAL: Learning Training Dynamics for Active LearningCode0
On the Convergence of Loss and Uncertainty-based Active Learning AlgorithmsCode0
Extracting Commonsense Properties from Embeddings with Limited Human GuidanceCode0
Face: Fast, Accurate and Context-Aware Audio Annotation and ClassificationCode0
REAL: A Representative Error-Driven Approach for 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