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 …

Vision transformers have become popular as a possible substitute to convolutional neural
networks (CNNs) for a variety of computer vision applications. These transformers, with their …

This work studies the recently proposed challenging and practical Multi-class Unsupervised
Anomaly Detection (MUAD) task, which only requires normal images for training while …

Fifth Generation (5G) networks adopt the security-by-design principle to provide highly
secure and robust services. 5G Core (5GC) uses the Hypertext Transfer Protocol version 2 …

In industrial quality assessment, monitoring whether the textured product contains defects is
a critical step. Compared to a large number of defect-free images that are easy to obtain …

With the impetus of Industry 5.0, smart manufacturing would witness the significant
advancement of automation and digital transformation. Even though Deep Learning (DL) …

One of the most promising use-cases for machine learning in industrial manufacturing is the
early detection of defective products using a quality control system. Such a system can save …

This work studies a challenging and practical issue known as multi-class unsupervised
anomaly detection (MUAD). This problem requires only normal images for training while …

Prebaked carbon anodes are a critical consumable in the aluminum electrolysis industry.
Prebaked carbon anode paste is the intermediate product of the prebaked carbon anode …

Existing industrial image anomaly detection techniques predominantly utilize codecs based
on convolutional neural networks (CNNs). However, traditional convolutional autoencoders …