Visual Anomaly Detection for Images: A Survey
It addresses the problem of summarizing and organizing existing research for researchers in machine learning and computer vision, but it is incremental as it does not introduce new methods or results.
This paper provides a comprehensive survey of classical and deep learning-based approaches for visual anomaly detection in images, grouping methods by their underlying principles and discussing their assumptions, advantages, and disadvantages to help researchers understand common principles and identify promising directions.
Visual anomaly detection is an important and challenging problem in the field of machine learning and computer vision. This problem has attracted a considerable amount of attention in relevant research communities. Especially in recent years, the development of deep learning has sparked an increasing interest in the visual anomaly detection problem and brought a great variety of novel methods. In this paper, we provide a comprehensive survey of the classical and deep learning-based approaches for visual anomaly detection in the literature. We group the relevant approaches in view of their underlying principles and discuss their assumptions, advantages, and disadvantages carefully. We aim to help the researchers to understand the common principles of visual anomaly detection approaches and identify promising research directions in this field.