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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 21–30 of 155 papers

TitleStatusHype
SLA^2P: Self-supervised Anomaly Detection with Adversarial PerturbationCode1
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and LocalizationCode1
Explainable Deep Few-shot Anomaly Detection with Deviation NetworksCode1
DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly DetectionCode1
Feature Encoding with AutoEncoders for Weakly-supervised Anomaly DetectionCode1
Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active LearningCode1
Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour modelsCode1
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly DataCode1
A^3: Activation Anomaly AnalysisCode1
Semi-supervised Anomaly Detection using AutoEncodersCode1
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