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 …

Unsupervised representation learning has proved to be a critical component of anomaly
detection/localization in images. The challenges to learn such a representation are two-fold …

In industrial manufacturing processes, errors frequently occur at unpredictable times and in
unknown manifestations. We tackle this problem, known as automatic defect detection …

The detection of manufacturing errors is crucial in fabrication processes to ensure product
quality and safety standards. Since many defects occur very rarely and their characteristics …

We introduce a powerful student-teacher framework for the challenging problem of
unsupervised anomaly detection and pixel-precise anomaly segmentation in high-resolution …

Visual anomaly detection, an important problem in computer vision, is usually formulated as
a one-class classification and segmentation task. The student-teacher (ST) framework has …

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 …

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

Anomaly Detection (AD) in images is a fundamental computer vision problem and refers to
identifying images and/or image substructures that deviate significantly from the norm …

Anomaly analysis is an important component of any surveillance system. In recent years, it
has drawn the attention of the computer vision and machine learning communities. In this …