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 20212030 of 3073 papers

TitleStatusHype
Importance of Self-Consistency in Active Learning for Semantic Segmentation0
Active Classification with Uncertainty Comparison QueriesCode0
Dual Adversarial Network for Deep Active Learning0
Weight Decay Scheduling and Knowledge Distillation for Active Learning0
Two Stream Active Query Suggestion for Active Learning in Connectomics0
Cross-context News Corpus for Protest Events related Knowledge Base ConstructionCode0
Learning to Rank for Active Learning: A Listwise Approach0
Is there something I'm missing? Topic Modeling in eDiscovery0
On Deep Unsupervised Active Learning0
Active Learning for Video Description With Cluster-Regularized Ensemble Ranking0
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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