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 351360 of 3073 papers

TitleStatusHype
Controllable Textual Inversion for Personalized Text-to-Image GenerationCode0
Continual egocentric object recognitionCode0
Conversational Disease Diagnosis via External Planner-Controlled Large Language ModelsCode0
Cost-Effective Active Learning for Melanoma SegmentationCode0
Crowd Counting With Partial Annotations in an ImageCode0
Deep Active Learning: Unified and Principled Method for Query and TrainingCode0
Constrained Multi-objective Bayesian Optimization through Optimistic Constraints EstimationCode0
A Cross-Domain Benchmark for Active LearningCode0
Constraining the Parameters of High-Dimensional Models with Active LearningCode0
Active DOP: A constituency treebank annotation tool with online learningCode0
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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