Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Anomaly detection (AD) is a crucial task in machine learning with various applications, such
as detecting emerging diseases, identifying financial frauds, and detecting fake news …

In this tutorial we aim to present a comprehensive survey of the advances in deep learning
techniques specifically designed for anomaly detection (deep anomaly detection for short) …

Though anomaly detection (AD) can be viewed as a classification problem (nominal vs.
anomalous) it is usually treated in an unsupervised manner since one typically does not …

The considerable significance of Anomaly Detection (AD) problem has recently drawn the
attention of many researchers. Consequently, the number of proposed methods in this …

Anomaly Detection (AD) in images is a fundamental computer vision problem and refers to
identifying images and/or image substructures that deviate significantly from the norm …

Given a long list of anomaly detection algorithms developed in the last few decades, how do
they perform with regard to (i) varying levels of supervision,(ii) different types of anomalies …

The detection of anomalous structures in natural image data is of utmost importance for
numerous tasks in the field of computer vision. The development of methods for …

Deep learning (DL) techniques have recently found success in anomaly detection (AD)
across various fields such as finance, medical services, and cloud computing. However …

Deep Learning (DL) is vulnerable to out-of-distribution and adversarial examples resulting in
incorrect outputs. To make DL more robust, several posthoc anomaly detection techniques …