Self-Supervised Anomaly Detection
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Benchmarks
KolektorSDD2
Most implemented
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited Data
Confidence-Aware and Self-Supervised Image Anomaly Localisation
Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Success
Self-Supervised Anomaly Detection by Self-Distillation and Negative Sampling
Papers
VACE: Learning Geometrically Structured Representations for Time Series Anomaly Detection
Anomaly detection in multivariate time series is a critical task across a wide range of real-world applications, where abnormal behaviour is rare, labels are unavailable, and the cost of a miss is high. The central chall…
Self-Supervised Anomaly DetectionTime Series Anomaly DetectionSelf-Supervised LearningLayer-Specific Lipschitz Modulation for Fault-Tolerant Multimodal Representation Learning
Modern multimodal systems deployed in industrial and safety-critical environments must remain reliable under partial sensor failures, signal degradation, or cross-modal inconsistencies. This work introduces a mathematica…
Self-Supervised Anomaly DetectionRepresentation LearningDemographic-Aware Self-Supervised Anomaly Detection Pretraining for Equitable Rare Cardiac Diagnosis
Rare cardiac anomalies are difficult to detect from electrocardiograms (ECGs) due to their long-tailed distribution with extremely limited case counts and demographic disparities in diagnostic performance. These limitati…
Self-Supervised Anomaly DetectionRepresentation LearningECG ClassificationVariational Autoencoders for P-wave Detection on Strong Motion Earthquake Spectrograms
Accurate P-wave detection is critical for earthquake early warning, yet strong-motion records pose challenges due to high noise levels, limited labeled data, and complex waveform characteristics. This study reframes P-wa…
Self-Supervised Anomaly DetectionISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects
Automatic visual inspection using machine learning-based methods plays a key role in achieving zero-defect policies in industry. Research on anomaly detection approaches is constrained by the availability of datasets tha…
Anomaly DetectionDefect DetectionSelf-Supervised Anomaly DetectionSupervised Anomaly Detection+3Meta Learning-Driven Iterative Refinement for Robust Anomaly Detection in Industrial Inspection
This study investigates the performance of robust anomaly detection models in industrial inspection, focusing particularly on their ability to handle noisy data. We propose to leverage the adaptation ability of meta lear…
Anomaly DetectionMeta-LearningSelf-Supervised Anomaly Detection