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 211220 of 3073 papers

TitleStatusHype
Multi-Anchor Active Domain Adaptation for Semantic SegmentationCode1
Semi-Supervised Active Learning with Temporal Output DiscrepancyCode1
ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic SegmentationCode1
Revisiting Uncertainty-based Query Strategies for Active Learning with TransformersCode1
Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question AnsweringCode1
SIMILAR: Submodular Information Measures Based Active Learning In Realistic ScenariosCode1
TableSense: Spreadsheet Table Detection with Convolutional Neural NetworksCode1
TagRuler: Interactive Tool for Span-Level Data Programming by DemonstrationCode1
Stochastic Batch Acquisition: A Simple Baseline for Deep Active LearningCode1
Quality-Aware Memory Network for Interactive Volumetric Image SegmentationCode1
Show:102550
← PrevPage 22 of 308Next →

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