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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 71–80 of 155 papers

TitleStatusHype
Weakly Supervised Detection of Baby Cry—0
Few-shot Weakly-supervised Cybersecurity Anomaly Detection—0
AGAD: Adversarial Generative Anomaly Detection—0
Zero-shot domain adaptation of anomalous samples for semi-supervised anomaly detection—0
Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection—0
Confidence-Aware and Self-Supervised Image Anomaly LocalisationCode0
Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption—0
Weakly Supervised Anomaly Detection: A SurveyCode1
Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial NetworksCode0
Look Around for Anomalies: Weakly-Supervised Anomaly Detection via Context-Motion Relational Learning—0
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