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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 31–40 of 155 papers

TitleStatusHype
CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection—0
Anomaly Detection with Domain Adaptation—0
Bridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies—0
Anomaly Detection in File Fragment Classification of Image File Formats—0
Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection—0
Brain Tumor Anomaly Detection via Latent Regularized Adversarial Network—0
BadSAD: Clean-Label Backdoor Attacks against Deep Semi-Supervised Anomaly Detection—0
Anomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis Through Self-Supervised Learning—0
Automated Processing of eXplainable Artificial Intelligence Outputs in Deep Learning Models for Fault Diagnostics of Large Infrastructures—0
Autoencoding Binary Classifiers for Supervised Anomaly Detection—0
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