Abstract
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.