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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 81–90 of 155 papers

TitleStatusHype
On the Impact of Object and Sub-component Level Segmentation Strategies for Supervised Anomaly Detection within X-ray Security Imagery—0
Open-Set Graph Anomaly Detection via Normal Structure Regularisation—0
Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Models—0
Prototypical Residual Networks for Anomaly Detection and Localization—0
Qsco: A Quantum Scoring Module for Open-set Supervised Anomaly Detection—0
RADE: Resource-Efficient Supervised Anomaly Detection Using Decision Tree-Based Ensemble Methods—0
A Radon-Nikodým Perspective on Anomaly Detection: Theory and Implications—0
Reconstruction Error-based Anomaly Detection with Few Outlying Examples—0
Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption—0
SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection—0
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