论文标题
持续的学习方法用于异常检测
Continual Learning Approaches for Anomaly Detection
论文作者
论文摘要
异常检测是一个相关的问题，它在众多现实世界应用中出现，尤其是在处理图像时。但是，在持续学习环境中，这项任务几乎没有研究。在这项工作中，我们介绍了一种称为“秤”的新方法（足够比例），以在连续学习环境中进行异常检测的框架中进行压缩重播。提出的技术缩放并使用超级分辨率模型压缩原始图像，据我们所知，该模型在不断学习的环境中首次研究。比例可以达到高水平的压缩，同时保持高水平的图像重建质量。结合其他异常检测方法，它可以实现最佳结果。为了验证所提出的方法，我们使用具有基于像素的异常的图像的真实数据集，并具有在不断学习的背景下为异常检测提供可靠的基准测试的范围，这是该领域进一步进步的基础。
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.
