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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
HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection—0
Deep Anomaly Detection and Search via Reinforcement Learning—0
Semi-Supervised Anomaly Detection Based on Quadratic Multiform Separation—0
Locality-aware Attention Network with Discriminative Dynamics Learning for Weakly Supervised Anomaly Detection—0
A One-Class Classification method based on Expanded Non-Convex HullsCode0
R2-AD2: Detecting Anomalies by Analysing the Raw GradientCode0
Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook—0
Unseen Anomaly Detection on Networks via Multi-Hypersphere LearningCode0
Semi-supervised anomaly detection algorithm based on KL divergence (SAD-KL)—0
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos—0
No Shifted Augmentations (NSA): compact distributions for robust self-supervised Anomaly Detection—0
LesionPaste: One-Shot Anomaly Detection for Medical Images—0
Abuse and Fraud Detection in Streaming Services Using Heuristic-Aware Machine Learning—0
Anomaly Detection in File Fragment Classification of Image File Formats—0
Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos—0
Self-Supervised Anomaly Detection by Self-Distillation and Negative SamplingCode0
A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods—0
No Shifted Augmentations (NSA): strong baselines for self-supervised Anomaly Detection—0
A Comparison of Supervised and Unsupervised Deep Learning Methods for Anomaly Detection in ImagesCode0
From Unsupervised to Semi-supervised Anomaly Detection Methods for HRRP Targets—0
Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection—0
Hop-Count Based Self-Supervised Anomaly Detection on Attributed NetworksCode0
Elsa: Energy-based learning for semi-supervised anomaly detection—0
Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection—0
Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-raysCode0
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