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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 111–120 of 155 papers

TitleStatusHype
Weakly Supervised Anomaly Detection via Knowledge-Data Alignment—0
Weakly Supervised Detection of Baby Cry—0
Anomaly Detection in Images—0
LesionPaste: One-Shot Anomaly Detection for Medical Images—0
Locality-aware Attention Network with Discriminative Dynamics Learning for Weakly Supervised Anomaly Detection—0
LogELECTRA: Self-supervised Anomaly Detection for Unstructured Logs—0
Look Around for Anomalies: Weakly-Supervised Anomaly Detection via Context-Motion Relational Learning—0
IgCONDA-PET: Weakly-Supervised PET Anomaly Detection using Implicitly-Guided Attention-Conditional Counterfactual Diffusion Modeling -- a Multi-Center, Multi-Cancer, and Multi-Tracer StudyCode0
Label-based Graph Augmentation with Metapath for Graph Anomaly DetectionCode0
Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial NetworksCode0
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