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Self-Supervised Anomaly Detection

Self-Supervision towards anomaly detection

Papers

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Showing 1–29 of 29 papers

TitleStatusHype
Self-supervised Anomaly Detection Pretraining Enhances Long-tail ECG DiagnosisCode2
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and LocalizationCode1
Iterative weak/self-supervised classification framework for abnormal events detectionCode1
SLA^2P: Self-supervised Anomaly Detection with Adversarial PerturbationCode1
Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks—0
End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection—0
Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategy—0
Hyperbolic Anomaly Detection—0
ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects—0
LogELECTRA: Self-supervised Anomaly Detection for Unstructured Logs—0
Meta Learning-Driven Iterative Refinement for Robust Anomaly Detection in Industrial Inspection—0
No Shifted Augmentations (NSA): compact distributions for robust self-supervised Anomaly Detection—0
No Shifted Augmentations (NSA): strong baselines for self-supervised Anomaly Detection—0
MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network—0
Anomaly Detection by Context Contrasting—0
Anomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis Through Self-Supervised Learning—0
CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection—0
SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection—0
Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook—0
Self-Supervised Anomaly Detection in the Wild: Favor Joint Embeddings Methods—0
Self-Supervised Anomaly Detection of Rogue Soil Moisture Sensors—0
Self-Supervised Iterative Refinement for Anomaly Detection in Industrial Quality Control—0
SeMAnD: Self-Supervised Anomaly Detection in Multimodal Geospatial Datasets—0
Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of SuccessCode0
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited DataCode0
Confidence-Aware and Self-Supervised Image Anomaly LocalisationCode0
Self-Supervised Anomaly Detection by Self-Distillation and Negative SamplingCode0
Hop-Count Based Self-Supervised Anomaly Detection on Attributed NetworksCode0
Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-raysCode0
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