Document worth reading: “Deep Learning for Anomaly Detection: A Survey”

Anomaly detection is an important disadvantage that has been well-studied inside quite a few evaluation areas and utility domains. The objective of this survey is two-fold, firstly we present a structured and full overview of study methods in deep learning-based anomaly detection. Furthermore, we evaluation the adoption of these methods for anomaly all through different utility domains and assess their effectiveness. We have grouped state-of-the-art evaluation methods into completely completely different lessons based totally on the underlying assumptions and technique adopted. Within each class we outline the elemental anomaly detection technique, along with its variants and present key assumptions, to tell apart between common and anomalous conduct. For each class, we present we moreover present the advantages and limitations and speak concerning the computational complexity of the methods in precise utility domains. Finally, we outline open factors in evaluation and challenges confronted whereas adopting these methods. Deep Learning for Anomaly Detection: A Survey