  Anomaly Detection is a relevant problem in numerous real-world applications,
especially when dealing with images. However, little attention has been paid to
the issue of changes over time in the input data distribution, which may cause
a significant decrease in performance. In this study, we investigate the
problem of Pixel-Level Anomaly Detection in the Continual Learning setting,
where new data arrives over time and the goal is to perform well on new and old
data. We implement several state-of-the-art techniques to solve the Anomaly
Detection problem in the classic setting and adapt them to work in the
Continual Learning setting. To validate the approaches, we use a real-world
dataset of images with pixel-based anomalies to provide a reliable benchmark
and serve as a foundation for further advancements in the field. We provide a
comprehensive analysis, discussing which Anomaly Detection methods and which
families of approaches seem more suitable for the Continual Learning setting.
