什么造就了用于少样本无监督图像异常检测的良好数据增强？
What makes a good data augmentation for few-shot unsupervised image anomaly detection?
AI总结：
本文通过实验系统评估多种数据增强对少样本无监督工业图像异常检测算法的影响，发现具体增强方式影响有限且组合增强未必提升准确率，并据此给出选择指导。
摘要：
数据增强是工业应用中无监督异常检测的一项很有前景的技术；在这些应用中，由于商业竞争和样本采集困难等因素，正样本的可用性往往受到限制。本文研究了如何为无监督异常检测有效选择和应用数据增强方法。通过实验，系统考察了各种数据增强方法对不同异常检测算法的影响。实验结果表明，不同工业图像异常检测（称为 IAD）算法的性能并不会因所采用的具体数据增强方法而受到显著影响；而且，组合多种数据增强方法并不一定能进一步提高异常检测的准确率，尽管它可以在特定方法上取得优异结果。这些发现为在 IAD 中针对不同需求选择合适的数据增强方法提供了有益指导。
英文摘要：
Data augmentation is a promising technique for unsupervised anomaly detection in industrial applications, where the availability of positive samples is often limited due to factors such as commercial competition and sample collection difficulties. In this paper, how to effectively select and apply data augmentation methods for unsupervised anomaly detection is studied. The impact of various data augmentation methods on different anomaly detection algorithms is systematically investigated through experiments. The experimental results show that the performance of different industrial image anomaly detection (termed as IAD) algorithms is not significantly affected by the specific data augmentation method employed and that combining multiple data augmentation methods does not necessarily yield further improvements in the accuracy of anomaly detection, although it can achieve excellent results on specific methods. These findings provide useful guidance on selecting appropriate data augmentation methods for different requirements in IAD.
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。
