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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 121–130 of 155 papers

TitleStatusHype
Deep Semi-Supervised Anomaly DetectionCode0
Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-raysCode0
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited DataCode0
Machine learning-based identification of Gaia astrometric exoplanet orbitsCode0
MAPL: Memory Augmentation and Pseudo-Labeling for Semi-Supervised Anomaly DetectionCode0
ADFA: Attention-augmented Differentiable top-k Feature Adaptation for Unsupervised Medical Anomaly DetectionCode0
Weakly-Supervised Video Anomaly Detection with Snippet Anomalous AttentionCode0
MTFL: Multi-Timescale Feature Learning for Weakly-Supervised Anomaly Detection in Surveillance VideosCode0
Weakly Supervised Anomaly Detection for Chest X-Ray ImageCode0
How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?Code0
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