Continual Learning Approaches for Anomaly Detection
This work addresses anomaly detection in continual learning for real-world image applications, providing a new benchmark, but it is incremental as it adapts existing techniques to a specific setting.
The paper tackles the problem of anomaly detection in continual learning settings by introducing SCALE, a novel compressed replay technique that scales and compresses images using a super-resolution model, achieving high compression while maintaining reconstruction quality and optimal results with other anomaly detection methods.
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.