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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 101–125 of 155 papers

TitleStatusHype
AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by Random Labeling—0
A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels—0
Autoencoding Binary Classifiers for Supervised Anomaly Detection—0
Automated Processing of eXplainable Artificial Intelligence Outputs in Deep Learning Models for Fault Diagnostics of Large Infrastructures—0
BadSAD: Clean-Label Backdoor Attacks against Deep Semi-Supervised Anomaly Detection—0
Brain Tumor Anomaly Detection via Latent Regularized Adversarial Network—0
Bridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies—0
CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection—0
Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection—0
CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection—0
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos—0
Deep Anomaly Detection and Search via Reinforcement Learning—0
Deep evolving semi-supervised anomaly detection—0
Deep Multi-Task Learning for Anomalous Driving Detection Using CAN Bus Scalar Sensor Data—0
Deep Semi-Supervised Anomaly Detection for Finding Fraud in the Futures Market—0
Directional anomaly detection—0
Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection—0
Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection—0
Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers—0
Efficient Quantum One-Class Support Vector Machines for Anomaly Detection Using Randomized Measurements and Variable Subsampling—0
Elsa: Energy-based learning for semi-supervised anomaly detection—0
End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection—0
Enhanced semi-supervised stamping process monitoring with physically-informed feature extraction—0
ESAD: End-to-end Deep Semi-supervised Anomaly Detection—0
Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategy—0
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