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 611620 of 3073 papers

TitleStatusHype
Advancements in Content-Based Image Retrieval: A Comprehensive Survey of Relevance Feedback Techniques0
Fair Active Learning in Low-Data Regimes0
Active learning with biased non-response to label requests0
SE(3)-Invariant Multiparameter Persistent Homology for Chiral-Sensitive Molecular Property Prediction0
Semi-supervised Active Learning for Video Action DetectionCode0
Benchmarking of Query Strategies: Towards Future Deep Active LearningCode0
PALS: Personalized Active Learning for Subjective Tasks in NLPCode0
ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation0
A Review of Machine Learning Methods Applied to Video Analysis Systems0
Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding0
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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