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 881890 of 3073 papers

TitleStatusHype
Semi-supervised Active Learning for Video Action DetectionCode0
Benchmarking of Query Strategies: Towards Future Deep Active LearningCode0
A Review of Machine Learning Methods Applied to Video Analysis Systems0
ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation0
Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding0
PALS: Personalized Active Learning for Subjective Tasks in NLPCode0
Transferable Candidate Proposal with Bounded UncertaintyCode0
A Structural-Clustering Based Active Learning for Graph Neural NetworksCode0
Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes0
Few Clicks Suffice: Active Test-Time Adaptation for Semantic Segmentation0
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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