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 14411450 of 3073 papers

TitleStatusHype
CitySurfaces: City-Scale Semantic Segmentation of Sidewalk MaterialsCode1
DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote SensingCode1
One-Bit Active Query With Contrastive Pairs0
Meta Agent Teaming Active Learning for Pose Estimation0
DeepVisualInsight: Time-Travelling Visualization for Spatio-Temporal Causality of Deep Classification Training0
MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active LearningCode0
Active Learning of Quantum System Hamiltonians yields Query Advantage0
Active Learning-Based Optimization of Scientific Experimental Design0
Embodied Learning for Lifelong Visual Perception0
Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class AnnealingCode0
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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