面向异常检测的持续学习方法
Continual Learning Approaches for Anomaly Detection
AI总结：
针对持续学习场景下图像异常检测研究不足的问题，本文提出 SCALE 压缩回放方法，利用超分辨率模型缩放压缩图像，并构建真实像素级异常基准，验证其在保持重建质量的同时可取得最优效果。
摘要：
异常检测是众多实际应用中都会出现的一个相关问题，尤其是在处理图像时。然而，在持续学习（Continual Learning）场景下针对该任务的研究还很少。在本工作中，我们提出了一种名为 SCALE（SCALing is Enough，缩放即足够）的新颖方法，用于在持续学习场景下的异常检测框架中执行压缩回放（Compressed Replay）。所提出的技术使用一个超分辨率（Super Resolution）模型对原始图像进行缩放和压缩；据我们所知，这是该模型首次在持续学习场景中得到研究。SCALE 能够在实现高压缩水平的同时保持高水平的图像重建质量。将其与其他异常检测方法结合使用，可以取得最优结果。为验证所提出的方法，我们使用了一个包含基于像素的异常的真实世界图像数据集，其范围旨在为持续学习背景下的异常检测提供一个可靠的基准，并作为该领域进一步发展的基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
