Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

We present a new framework for Patch Distribution Modeling, PaDiM, to concurrently detect
and localize anomalies in images in a one-class learning setting. PaDiM makes use of a …

Anomaly detection is commonly pursued as a one-class classification problem, where
models can only learn from normal training samples, while being evaluated on both normal …

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

Recent years have witnessed the flourishing of deep learning-based methods in
hyperspectral anomaly detection (HAD). However, the lack of available supervision …

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 …

This paper considers few-shot anomaly detection (FSAD), a practical yet under-studied
setting for anomaly detection (AD), where only a limited number of normal images are …

The comprehensive intelligent development of the manufacturing industry puts forward new
requirements for the quality inspection of industrial products. This paper summarizes the …

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 …