Anomaly detection, aka outlier detection or novelty detection, has been a lasting yet active
research area in various research communities for several decades. There are still some …

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 …

Visual surface anomaly detection aims to detect local image regions that significantly
deviate from normal appearance. Recent surface anomaly detection methods rely on …

Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …

Deep autoencoder has been extensively used for anomaly detection. Training on the normal
data, the autoencoder is expected to produce higher reconstruction error for the abnormal …

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 …

Enormous amounts of data are being produced everyday by sub-meters and smart sensors
installed in residential buildings. If leveraged properly, that data could assist end-users …

Over the recent years, the growth of online social media has greatly facilitated the way
people communicate with each other. Users of online social media share information …

We introduce a powerful student-teacher framework for the challenging problem of
unsupervised anomaly detection and pixel-precise anomaly segmentation in high-resolution …

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 …