Self-Supervised Anomaly Detection
6 papers with code • 0 benchmarks • 0 datasets
Self-Supervision towards anomaly detection
These leaderboards are used to track progress in Self-Supervised Anomaly Detection
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.
The detection of abnormal events in surveillance footage remains a challenge and has been the scope of various research works.
Detecting anomalies in musculoskeletal radiographs is of paramount importance for large-scale screening in the radiology workflow.
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.
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
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.