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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
Supervised Anomaly Detection Method Combining Generative Adversarial Networks and Three-Dimensional Data in Vehicle Inspections—0
Prototypical Residual Networks for Anomaly Detection and Localization—0
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch—0
HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection—0
Unsupervised Model Selection for Time-series Anomaly DetectionCode1
Deep Anomaly Detection and Search via Reinforcement Learning—0
Semi-Supervised Anomaly Detection Based on Quadratic Multiform Separation—0
Locality-aware Attention Network with Discriminative Dynamics Learning for Weakly Supervised Anomaly Detection—0
Consistency-based Self-supervised Learning for Temporal Anomaly LocalizationCode1
A One-Class Classification method based on Expanded Non-Convex HullsCode0
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