Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

Currently, deep learning-based visual inspection has been highly successful with the help of
supervised learning methods. However, in real industrial scenarios, the scarcity of defect …

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 …

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

We present a new framework for Patch Distribution Modeling, PaDiM, to concurrently detect
and localize anomalies in images in a one-class learning setting. PaDiM makes use of a …

Recent developments in maintenance modelling fueled by data-based approaches such as
machine learning (ML), have enabled a broad range of applications. In the automotive …

Visual anomaly detection addresses the problem of classification or localization of regions in
an image that deviate from their normal appearance. A popular approach trains an auto …

Reconstruction-based methods play an important role in unsupervised anomaly detection in
images. Ideally, we expect a perfect reconstruction for normal samples and poor …

Density-based and classification-based methods have ruled unsupervised anomaly
detection in recent years, while reconstruction-based methods are rarely mentioned for the …

With the recent advances in deep neural networks, anomaly detection in multimedia has
received much attention in the computer vision community. While reconstruction-based …