Self-Supervised Anomaly Detection

5 papers with code • 0 benchmarks • 0 datasets

Self-Supervision towards anomaly detection

Most implemented papers

Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization

hmsch/natural-synthetic-anomalies 30 Sep 2021

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 3 Jan 2021

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 19 Feb 2021

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 16 Apr 2021

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.

Self-Supervised Anomaly Detection by Self-Distillation and Negative Sampling

black0017/black0017 17 Jan 2022

Detecting whether examples belong to a given in-distribution or are Out-Of-Distribution (OOD) requires identifying features specific to the in-distribution.