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 4150 of 3073 papers

TitleStatusHype
dopanim: A Dataset of Doppelganger Animals with Noisy Annotations from Multiple HumansCode1
Transductive Active Learning with Application to Safe Bayesian OptimizationCode1
Dataset Quantization with Active Learning based Adaptive SamplingCode1
ALPBench: A Benchmark for Active Learning Pipelines on Tabular DataCode1
Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center DatasetCode1
SS-ADA: A Semi-Supervised Active Domain Adaptation Framework for Semantic SegmentationCode1
Understanding active learning of molecular docking and its applicationsCode1
CloudS2Mask: A novel deep learning approach for improved cloud and cloud shadow masking in Sentinel-2 imageryCode1
Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)Code1
Making Better Use of Unlabelled Data in Bayesian Active LearningCode1
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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