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 111120 of 3073 papers

TitleStatusHype
Exploring Active 3D Object Detection from a Generalization PerspectiveCode1
Active learning for medical image segmentation with stochastic batchesCode1
MoBYv2AL: Self-supervised Active Learning for Image ClassificationCode1
Are Binary Annotations Sufficient? Video Moment Retrieval via Hierarchical Uncertainty-Based Active LearningCode1
Man-recon: manifold learning for reconstruction with deep autoencoder for smart seismic interpretationCode1
MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object DetectionCode1
Knowledge-Aware Federated Active Learning with Non-IID DataCode1
PyTAIL: Interactive and Incremental Learning of NLP Models with Human in the Loop for Online DataCode1
Plug and Play Active Learning for Object DetectionCode1
Finding active galactic nuclei through FinkCode1
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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