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 25212530 of 3073 papers

TitleStatusHype
Continual Deep Active Learning for Medical Imaging: Replay-Base Architecture for Context AdaptationCode0
High-contrast “gaudy” images improve the training of deep neural network models of visual cortexCode0
Adversarial Representation Active LearningCode0
Cross-context News Corpus for Protest Events related Knowledge Base ConstructionCode0
Cross-layer Optimization for High Speed Adders: A Pareto Driven Machine Learning ApproachCode0
PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and ClassificationCode0
Understanding Uncertainty SamplingCode0
Aspect-based Sentiment Analysis of Scientific ReviewsCode0
Crowd Counting With Partial Annotations in an ImageCode0
Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local FilterCode0
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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