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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 101–110 of 155 papers

TitleStatusHype
AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by Random Labeling—0
A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels—0
Autoencoding Binary Classifiers for Supervised Anomaly Detection—0
Automated Processing of eXplainable Artificial Intelligence Outputs in Deep Learning Models for Fault Diagnostics of Large Infrastructures—0
BadSAD: Clean-Label Backdoor Attacks against Deep Semi-Supervised Anomaly Detection—0
Brain Tumor Anomaly Detection via Latent Regularized Adversarial Network—0
Bridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies—0
CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection—0
Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection—0
CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection—0
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