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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 131–140 of 155 papers

TitleStatusHype
Brain Tumor Anomaly Detection via Latent Regularized Adversarial Network—0
Anomaly Detection with Domain Adaptation—0
A Characteristic Function for Shapley-Value-Based Attribution of Anomaly ScoresCode0
A^3: Activation Anomaly AnalysisCode1
Semi-supervised Anomaly Detection on Attributed GraphsCode0
Semi-supervised Anomaly Detection using AutoEncodersCode1
AnoNet: Weakly Supervised Anomaly Detection in Textured Surfaces—0
On the Impact of Object and Sub-component Level Segmentation Strategies for Supervised Anomaly Detection within X-ray Security Imagery—0
Deep Weakly-supervised Anomaly DetectionCode0
RADE: Resource-Efficient Supervised Anomaly Detection Using Decision Tree-Based Ensemble Methods—0
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