面向边缘端视觉异常检测的内存高效持续学习
Memory Efficient Continual Learning for Edge-Based Visual Anomaly Detection
AI总结：
针对边缘设备资源受限及数据动态变化挑战，研究视觉异常检测的持续学习问题，发现PaSTe方法结合Compressed Replay技术在提升检测性能的同时大幅降低内存开销。
摘要：
视觉异常检测（VAD）是计算机视觉中的一项关键任务，具有众多现实世界应用。然而，将这些模型部署在边缘设备上面临着重大挑战，例如受限的计算和内存资源。此外，现实世界环境中的动态数据分布需要模型持续适应，进一步加剧了在有限资源下部署的复杂性。为了应对这些挑战，我们对边缘设备上视觉异常检测的持续学习（CLAD）问题进行了新颖的研究。我们评估了STFPM方法，因为其在边缘设备上具有较低的内存占用，结果表明该方法与Replay方法结合时展现出良好的性能。此外，我们提出研究最近提出的PaSTe方法的行为，该方法专为边缘端设计，但尚未在持续学习语境下被探索。我们的结果表明，PaSTe不仅是STFPM的轻量级版本，而且实现了更优的异常检测性能，结合Replay技术将像素级F1分数提升了10%。特别是，PaSTe的结构允许我们使用一系列Compressed Replay技术进行测试，与STFPM传统的Replay相比，内存开销最多减少了91.5%。我们的研究证明了在资源受限的边缘设备上，部署能够在CLAD场景中适应并增量学习的VAD模型是可行的。
英文摘要：
Visual Anomaly Detection (VAD) is a critical task in computer vision with numerous real-world applications. However, deploying these models on edge devices presents significant challenges, such as constrained computational and memory resources. Additionally, dynamic data distributions in real-world settings necessitate continuous model adaptation, further complicating deployment under limited resources. To address these challenges, we present a novel investigation into the problem of Continual Learning for Visual Anomaly Detection (CLAD) on edge devices. We evaluate the STFPM approach, given its low memory footprint on edge devices, which demonstrates good performance when combined with the Replay approach. Furthermore, we propose to study the behavior of a recently proposed approach, PaSTe, specifically designed for the edge but not yet explored in the Continual Learning context. Our results show that PaSTe is not only a lighter version of STPFM, but it also achieves superior anomaly detection performance, improving the f1 pixel performance by 10% with the Replay technique. In particular, the structure of PaSTe allows us to test it using a series of Compressed Replay techniques, reducing memory overhead by a maximum of 91.5% compared to the traditional Replay for STFPM. Our study proves the feasibility of deploying VAD models that adapt and learn incrementally on CLAD scenarios on resource-constrained edge devices.
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。
