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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 76–100 of 155 papers

TitleStatusHype
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
Supervised Anomaly Detection Method Combining Generative Adversarial Networks and Three-Dimensional Data in Vehicle Inspections—0
Prototypical Residual Networks for Anomaly Detection and Localization—0
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch—0
HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection—0
Unsupervised Model Selection for Time-series Anomaly DetectionCode1
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
Consistency-based Self-supervised Learning for Temporal Anomaly LocalizationCode1
A One-Class Classification method based on Expanded Non-Convex HullsCode0
Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly DetectionCode1
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
Catching Both Gray and Black Swans: Open-set Supervised Anomaly DetectionCode1
Semi-supervised anomaly detection algorithm based on KL divergence (SAD-KL)—0
Learning to Adapt to Unseen Abnormal Activities under Weak SupervisionCode1
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos—0
Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame DetectionCode1
No Shifted Augmentations (NSA): compact distributions for robust self-supervised Anomaly Detection—0
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