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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

Showing 151155 of 155 papers

TitleStatusHype
Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty DetectionCode0
A Comparison of Supervised and Unsupervised Deep Learning Methods for Anomaly Detection in ImagesCode0
Self-Supervised Anomaly Detection by Self-Distillation and Negative SamplingCode0
AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on AnomaliesCode0
Take Package as Language: Anomaly Detection Using TransformerCode0
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