Continual Learning Approaches for Anomaly Detection

• URL: http://arxiv.org/abs/2212.11192v2
• Date: Thu, 5 Sep 2024 18:17:51 GMT
• Title: Continual Learning Approaches for Anomaly Detection
• Authors: Davide Dalle Pezze, Eugenia Anello, Chiara Masiero, Gian Antonio Susto,
• Abstract summary: We introduce a novel approach 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. 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.
• Score: 6.014777261874645
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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