Continual Learning Approaches for Anomaly Detection
- URL: http://arxiv.org/abs/2212.11192v1
- Date: Wed, 21 Dec 2022 17:08:58 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: 2.599882743586164
- 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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