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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 51–75 of 155 papers

TitleStatusHype
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited DataCode0
IgCONDA-PET: Weakly-Supervised PET Anomaly Detection using Implicitly-Guided Attention-Conditional Counterfactual Diffusion Modeling -- a Multi-Center, Multi-Cancer, and Multi-Tracer StudyCode0
Unseen Anomaly Detection on Networks via Multi-Hypersphere LearningCode0
R2-AD2: Detecting Anomalies by Analysing the Raw GradientCode0
ADFA: Attention-augmented Differentiable top-k Feature Adaptation for Unsupervised Medical Anomaly DetectionCode0
Weakly-Supervised Anomaly Detection in the Milky WayCode0
Revisiting Non-separable Binary Classification and its Applications in Anomaly DetectionCode0
How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?Code0
MTFL: Multi-Timescale Feature Learning for Weakly-Supervised Anomaly Detection in Surveillance VideosCode0
A Characteristic Function for Shapley-Value-Based Attribution of Anomaly ScoresCode0
Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly DetectionCode0
Machine learning-based identification of Gaia astrometric exoplanet orbitsCode0
GANomaly: Semi-Supervised Anomaly Detection via Adversarial TrainingCode0
Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial NetworksCode0
MAPL: Memory Augmentation and Pseudo-Labeling for Semi-Supervised Anomaly DetectionCode0
Graph Fairing Convolutional Networks for Anomaly DetectionCode0
Semi-supervised Anomaly Detection via Adaptive Reinforcement Learning-Enabled Method with Causal Inference for Sensor SignalsCode0
Hop-Count Based Self-Supervised Anomaly Detection on Attributed NetworksCode0
From Unsupervised to Semi-supervised Anomaly Detection Methods for HRRP Targets—0
Few-shot Weakly-supervised Cybersecurity Anomaly Detection—0
CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection—0
Anomaly Detection with Domain Adaptation—0
Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategy—0
ESAD: End-to-end Deep Semi-supervised Anomaly Detection—0
Bridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies—0
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