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Supervised Anomaly Detection

In the training set, the amount of abnormal samples is limited and significant fewer than normal samples, producing data distributions that lead to a naturally imbalanced learning problem.

Papers

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Showing 111–120 of 155 papers

TitleStatusHype
Explainable Deep Few-shot Anomaly Detection with Deviation NetworksCode1
A Comparison of Supervised and Unsupervised Deep Learning Methods for Anomaly Detection in ImagesCode0
From Unsupervised to Semi-supervised Anomaly Detection Methods for HRRP Targets—0
DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly DetectionCode1
Feature Encoding with AutoEncoders for Weakly-supervised Anomaly DetectionCode1
Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection—0
Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active LearningCode1
Hop-Count Based Self-Supervised Anomaly Detection on Attributed NetworksCode0
Elsa: Energy-based learning for semi-supervised anomaly detection—0
Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection—0
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