A Comprehensive Review of Deep Anomaly Detection Techniques- An Analysis

The area of anomaly identification in images, using deep learning techniques, has garnered considerable interest in recent years owing to its extensive applicability across many fields. Anomaly Detection also referred to as outlier identification, aims to find data instances that display substantial deviations from the rest of the existing data. This paper provides a thorough analysis of prior research that has suggested approaches for identifying abnormalities in images and videos through the use of deep learning technologies. This paper emphasizes on a more detailed perspective of the differences in research efforts by examining factors such as the methods used for anomaly detection, the datasets used, the evaluation metrics used, and the approach used to get deep anomaly detection results. It concludes by discussing the difficulties and possible directions for future research on the detection of abnormalities.