Continual-MEGA: 一个大规模基准用于通用的持续异常检测
Continual-MEGA: A Large-scale Benchmark for Generalizable Continual Anomaly Detection
AI总结：
Continual-MEGA基准通过引入大规模数据集和零样本泛化场景，提升了持续异常检测的通用性和鲁棒性。
摘要：
在本文中，我们介绍了一个新的基准，用于持续学习中的异常检测，旨在更好地反映现实世界部署场景。我们的基准，Continual-MEGA，包含一个大规模且多样化的数据集，通过将精心挑选的现有数据集与我们新提出的数据集ContinualAD相结合，显著扩展了现有的评估设置。除了标准的持续学习扩展数量外，我们还提出了一种新的场景，该场景测量零样本泛化到未见过的类别，这些类别在持续适应过程中未被观察到。这种设置提出了一个新的问题设置，持续适应也增强了零样本性能。我们还提出了一个统一的基线算法，它在少量检测中提高了鲁棒性，并保持了强大的泛化能力。通过广泛的评估，我们报告了三个关键发现：(1)现有方法在像素级缺陷定位方面有显著的改进空间；(2)我们提出的方法始终优于先前的方法；(3)新引入的ContinualAD数据集增强了强大异常检测模型的性能。我们发布了基准和代码在https://github.com/Continual-Mega/Continual-Mega。
英文摘要：
In this paper, we introduce a new benchmark for continual learning in anomaly detection, aimed at better reflecting real-world deployment scenarios. Our benchmark, Continual-MEGA, includes a large and diverse dataset that significantly expands existing evaluation settings by combining carefully curated existing datasets with our newly proposed dataset, ContinualAD. In addition to standard continual learning with expanded quantity, we propose a novel scenario that measures zero-shot generalization to unseen classes, those not observed during continual adaptation. This setting poses a new problem setting that continual adaptation also enhances zero-shot performance. We also present a unified baseline algorithm that improves robustness in few-shot detection and maintains strong generalization. Through extensive evaluations, we report three key findings: (1) existing methods show substantial room for improvement, particularly in pixel-level defect localization; (2) our proposed method consistently outperforms prior approaches; and (3) the newly introduced ContinualAD dataset enhances the performance of strong anomaly detection models. We release the benchmark and code in https://github.com/Continual-Mega/Continual-Mega.
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。
