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 761770 of 3073 papers

TitleStatusHype
Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active LearningCode0
AI For Fraud Awareness0
Classification Committee for Active Deep Object Detection0
How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning0
Planning to Learn: A Novel Algorithm for Active Learning during Model-Based PlanningCode0
BI-LAVA: Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis0
Fast Risk Assessment in Power Grids through Novel Gaussian Process and Active Learning0
Active Bird2Vec: Towards End-to-End Bird Sound Monitoring with Transformers0
Composable Core-sets for Diversity Approximation on Multi-Dataset Streams0
Discrepancy-based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images0
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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