Self-Supervised Anomaly Detection
10 papers with code • 0 benchmarks • 0 datasets
Self-Supervision towards anomaly detection
Benchmarks
These leaderboards are used to track progress in Self-Supervised Anomaly Detection
Most implemented papers
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization
hmsch/natural-synthetic-anomalies
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We introduce a simple and intuitive self-supervision task, Natural Synthetic Anomalies (NSA), for training an end-to-end model for anomaly detection and localization using only normal training data.
Iterative weak/self-supervised classification framework for abnormal events detection
DegardinBruno/human_self_learning_anomaly
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The detection of abnormal events in surveillance footage remains a challenge and has been the scope of various research works.
Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-rays
antoine-spahr/SELF-TAUGHT-SEMI-SUPERVISED-ANOMALY-DETECTION
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Detecting anomalies in musculoskeletal radiographs is of paramount importance for large-scale screening in the radiology workflow.
Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks
Juintin/GraphAnomalyDetection
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Although various approaches have been proposed to solve this problem, two major limitations exist: (1) unsupervised approaches usually work much less efficiently due to the lack of supervisory signal, and (2) existing anomaly detection methods only use local contextual information to detect anomalous nodes, e. g., one- or two-hop information, but ignore the global contextual information.
SLA$^2$P: Self-supervised Anomaly Detection with Adversarial Perturbation
wyzjack/SLA2P
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Next we add adversarial perturbation to the transformed features to decrease their softmax scores of the predicted labels and design anomaly scores based on the predictive uncertainties of the classifier on these perturbed features.
Self-Supervised Anomaly Detection by Self-Distillation and Negative Sampling
Detecting whether examples belong to a given in-distribution or are Out-Of-Distribution (OOD) requires identifying features specific to the in-distribution.
Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Success
jaeminyoo/ssl-ad
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Self-supervised learning (SSL) has emerged as a promising alternative to create supervisory signals to real-world problems, avoiding the extensive cost of manual labeling.
Confidence-Aware and Self-Supervised Image Anomaly Localisation
Universal anomaly detection still remains a challenging problem in machine learning and medical image analysis.
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited Data
niamhbelton/ss-fewsome_disease_severity_knee_osteoarthritis
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This work proposes a three stage approach for automated continuous grading of knee OA that is built upon the principles of Anomaly Detection (AD); learning a robust representation of healthy knee X-rays and grading disease severity based on its distance to the centre of normality.
Self-supervised Anomaly Detection Pretraining Enhances Long-tail ECG Diagnosis
Current computer-aided ECG diagnostic systems struggle with the underdetection of rare but critical cardiac anomalies due to the imbalanced nature of ECG datasets.
