Subject
3 entries
Anomaly Detection
Bookmarks
Deep Anomaly Detection with Self-Supervised Learning and Adversarial Training
This paper combines self-supervised learning and adversarial training to improve deep anomaly detection, leveraging unlabeled normal data to learn representations that are robust to perturbations and more sensitive to out-of-distribution inputs. It matters because labeled anomaly data is rare in practice, making self-supervised approaches essential for real-world deployment.
Introducing Practical and Robust Anomaly Detection in a Time Series
Twitter's 2015 release of AnomalyDetection — an open-source R library using STL decomposition and the Generalized ESD test to find anomalies in time series. One of the first production-grade anomaly detection tools to be open-sourced by a major tech company.
Introduction to One-Class Support Vector Machines
A practical introduction to one-class SVMs — the variant of Support Vector Machines used for anomaly detection and novelty detection when you only have examples of normal behavior. Useful when collecting labeled anomaly examples is impractical or impossible.