  Anomaly Detection is a relevant problem that arises in numerous real-world
applications, especially when dealing with images. However, there has been
little research for this task in the Continual Learning setting. In this work,
we introduce a novel approach called SCALE (SCALing is Enough) to perform
Compressed Replay in a framework for Anomaly Detection in Continual Learning
setting. The proposed technique scales and compresses the original images using
a Super Resolution model which, to the best of our knowledge, is studied for
the first time in the Continual Learning setting. SCALE can achieve a high
level of compression while maintaining a high level of image reconstruction
quality. In conjunction with other Anomaly Detection approaches, it can achieve
optimal results. To validate the proposed approach, we use a real-world dataset
of images with pixel-based anomalies, with the scope to provide a reliable
benchmark for Anomaly Detection in the context of Continual Learning, serving
as a foundation for further advancements in the field.
