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 …

The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …

Detecting anomalies in images is an important task, especially in real-time computer vision
applications. In this work, we focus on computational efficiency and propose a lightweight …

Despite the rapid advance of unsupervised anomaly detection, existing methods require to
train separate models for different objects. In this work, we present UniAD that accomplishes …

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

Being able to spot defective parts is a critical component in large-scale industrial
manufacturing. A particular challenge that we address in this work is the cold-start problem …

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 …

We present a transformer-based image anomaly detection and localization network. Our
proposed model is a combination of a reconstruction-based approach and patch …

Machine learning models often encounter samples that are diverged from the training
distribution. Failure to recognize an out-of-distribution (OOD) sample, and consequently …

Anomaly detection is a challenging task and usually formulated as an one-class learning
problem for the unexpectedness of anomalies. This paper proposes a simple yet powerful …