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 …

In this work, by re-examining the “matching” nature of Anomaly Detection (AD), we propose
a novel AD framework that simultaneously enjoys new records of AD accuracy and …

Unified anomaly detection (AD) is one of the most valuable challenges for anomaly
detection, where one unified model is trained with normal samples from multiple classes …

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

Unsupervised anomaly detection techniques, which do not rely on prior knowledge of
anomalies, have attracted considerable attention in the field of industrial surface inspection …

Anomaly Detection is challenging as usually only the normal samples are seen during
training and the detector needs to discover anomalies on-the-fly. The recently proposed …

We focus on a specific use case in anomaly detection where the distribution of normal
samples is supported by a lower-dimensional manifold. Here, regularized autoencoders …

Texture is the surface qualities and visual attributes of an object, determined by the
arrangement, size, shape, density, and proportion of its fundamental components. In the …

This paper proposes the Adversarial Denoising Diffusion Model (ADDM). Diffusion models
excel at generating high-quality samples, outperforming other generative models. These …

Localizing defects in products is a critical component of industrial pipelines in
manufacturing, retail, and many other industries to ensure consistent delivery of high quality …