揭示不断变化世界中的异常：持续学习中像素级异常检测的基准
Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learning
AI总结：
本研究探讨了持续学习环境下的像素级异常检测问题，通过将多种最先进的技术适配至持续学习环境，并利用真实世界图像数据集提供可靠基准和全面分析，为该领域进一步发展奠定基础。
摘要：
异常检测在众多现实世界应用中是一个重要问题，尤其是在处理图像时。然而，输入数据分布随时间变化的问题却鲜受关注，这可能导致性能显著下降。在本研究中，我们探讨了持续学习环境下的像素级异常检测问题，其中新数据随时间不断到达，目标是在新数据和旧数据上均表现良好。我们实现了多种最先进的技术来解决经典环境下的异常检测问题，并对其进行调整以适应持续学习环境。为了验证这些方法，我们使用了一个包含基于像素异常的真实世界图像数据集，以提供可靠的基准，并作为该领域进一步发展的基础。我们提供了全面的分析，探讨了哪些异常检测方法以及哪些方法族似乎更适合持续学习环境。
英文摘要：
Anomaly Detection is a relevant problem in numerous real-world applications, especially when dealing with images. However, little attention has been paid to the issue of changes over time in the input data distribution, which may cause a significant decrease in performance. In this study, we investigate the problem of Pixel-Level Anomaly Detection in the Continual Learning setting, where new data arrives over time and the goal is to perform well on new and old data. We implement several state-of-the-art techniques to solve the Anomaly Detection problem in the classic setting and adapt them to work in the Continual Learning setting. To validate the approaches, we use a real-world dataset of images with pixel-based anomalies to provide a reliable benchmark and serve as a foundation for further advancements in the field. We provide a comprehensive analysis, discussing which Anomaly Detection methods and which families of approaches seem more suitable for the Continual Learning setting.
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。
