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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 26–50 of 155 papers

TitleStatusHype
Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active LearningCode1
Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour modelsCode1
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly DataCode1
A^3: Activation Anomaly AnalysisCode1
Semi-supervised Anomaly Detection using AutoEncodersCode1
Bridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies—0
Enhanced semi-supervised stamping process monitoring with physically-informed feature extraction—0
Automated Processing of eXplainable Artificial Intelligence Outputs in Deep Learning Models for Fault Diagnostics of Large Infrastructures—0
ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects—0
A Radon-Nikodým Perspective on Anomaly Detection: Theory and Implications—0
SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection—0
Semi-supervised Anomaly Detection with Extremely Limited Labels in Dynamic Graphs—0
Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection—0
BadSAD: Clean-Label Backdoor Attacks against Deep Semi-Supervised Anomaly Detection—0
Deep evolving semi-supervised anomaly detection—0
PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time SeriesCode0
SADDE: Semi-supervised Anomaly Detection with Dependable ExplanationsCode0
Take Package as Language: Anomaly Detection Using TransformerCode0
Weakly-Supervised Anomaly Detection in Surveillance Videos Based on Two-Stream I3D Convolution Network—0
Directional anomaly detection—0
MTFL: Multi-Timescale Feature Learning for Weakly-Supervised Anomaly Detection in Surveillance VideosCode0
Self-Supervised Anomaly Detection in the Wild: Favor Joint Embeddings Methods—0
Multi-Normal Prototypes Learning for Weakly Supervised Anomaly DetectionCode0
Efficient Quantum One-Class Support Vector Machines for Anomaly Detection Using Randomized Measurements and Variable Subsampling—0
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited DataCode0
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