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 24112420 of 3073 papers

TitleStatusHype
Disagreement-based Active Learning in Online Settings0
ProductNet: a Collection of High-Quality Datasets for Product Representation Learning0
Active Adversarial Domain Adaptation0
Exploring Representativeness and Informativeness for Active Learning0
Robust and Discriminative Labeling for Multi-label Active Learning Based on Maximum Correntropy Criterion0
Detecting Repeating Objects using Patch Correlation Analysis0
BAOD: Budget-Aware Object Detection0
Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification0
Active Learning for Decision-Making from Imbalanced Observational DataCode0
Context-Aware Query Selection for Active Learning in Event Recognition0
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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