In the social production system, image data are rapidly generated from almost all fields such
as factories, hospitals, and transportation, promoting higher requirements for image anomaly …

The recent rapid development of deep learning has laid a milestone in industrial image
anomaly detection (IAD). In this paper, we provide a comprehensive review of deep learning …

Self-supervised feature reconstruction methods have shown promising advances in
industrial image anomaly detection and localization. Despite this progress these methods …

Anomaly detection is crucial to the advanced identification of product defects such as
incorrect parts, misaligned components, and damages in industrial manufacturing. Due to …

Developing machine learning models that can detect and localize the unexpected or
anomalous structures within images is very important for numerous computer vision tasks …

Because anomalous samples cannot be used for training, many anomaly detection and
localization methods use pre-trained networks and non-parametric modeling to estimate …

We propose a simple and application-friendly network (called SimpleNet) for detecting and
localizing anomalies. SimpleNet consists of four components:(1) a pre-trained Feature …

Identifying defects in the images of industrial products has been an important task to
enhance quality control and reduce maintenance costs. In recent studies, industrial anomaly …

Industrial defect detection is commonly addressed with anomaly detection (AD) methods
where no or only incomplete data of potentially occurring defects is available. This work …

Image anomaly detection (IAD) is an emerging and vital computer vision task in industrial
manufacturing (IM). Recently, many advanced algorithms have been reported, but their …