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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 131–140 of 155 papers

TitleStatusHype
Semi-supervised Anomaly Detection on Attributed GraphsCode0
Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled DataCode0
A Characteristic Function for Shapley-Value-Based Attribution of Anomaly ScoresCode0
Hop-Count Based Self-Supervised Anomaly Detection on Attributed NetworksCode0
Semi-supervised Anomaly Detection via Adaptive Reinforcement Learning-Enabled Method with Causal Inference for Sensor SignalsCode0
Understanding Bias in Anomaly Detection: A Semi-Supervised View with PAC GuaranteesCode0
Unseen Anomaly Detection on Networks via Multi-Hypersphere LearningCode0
Graph Fairing Convolutional Networks for Anomaly DetectionCode0
Deep Weakly-supervised Anomaly DetectionCode0
Confidence-Aware and Self-Supervised Image Anomaly LocalisationCode0
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