Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learning

Nikola Bugarin, Jovana Bugaric, Manuel Barusco, Davide Dalle Pezze, Gian Antonio Susto; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4065-4074

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.