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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
Improving Interpretability of Scores in Anomaly Detection Based on Gaussian-Bernoulli Restricted Boltzmann Machine—0
ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects—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
Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection—0
MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network—0
Neural Batch Sampling with Reinforcement Learning for Semi-Supervised Anomaly Detection—0
No Shifted Augmentations (NSA): compact distributions for robust self-supervised Anomaly Detection—0
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