ATAC-Net：缩放视图对异常检测更有效
ATAC-Net: Zoomed view works better for Anomaly Detection
AI总结：
本文提出ATAC-Net框架，利用极少量已知异常样本并结合注意力引导裁剪，在可比设置中优于当前主流无监督异常检测方法。
摘要：
深度学习在视觉异常检测中的应用因其在质量控制和制造业中的潜在用途而广受欢迎。当前的标准方法是无监督方法，利用干净数据集在测试期间检测偏差并标记异常。然而，当异常类型事先已知时，纳入少量样本可以显著提升性能。因此，我们提出ATAC-Net，这是一个从极少量已知先验异常中训练检测异常的框架。此外，我们引入注意力引导裁剪，在训练阶段提供对可疑区域的更近距离观察。我们的框架是一个可靠且易于理解的异常检测系统，并且我们在可比设置中证实了其优于当前一些最先进技术。
英文摘要：
The application of deep learning in visual anomaly detection has gained widespread popularity due to its potential use in quality control and manufacturing. Current standard methods are Unsupervised, where a clean dataset is utilised to detect deviations and flag anomalies during testing. However, incorporating a few samples when the type of anomalies is known beforehand can significantly enhance performance. Thus, we propose ATAC-Net, a framework that trains to detect anomalies from a minimal set of known prior anomalies. Furthermore, we introduce attention-guided cropping, which provides a closer view of suspect regions during the training phase. Our framework is a reliable and easy-to-understand system for detecting anomalies, and we substantiate its superiority to some of the current state-of-the-art techniques in a comparable 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。
