What makes a good data augmentation for few-shot unsupervised image anomaly detection?
This provides practical guidance for selecting data augmentation in industrial anomaly detection, but it is incremental as it focuses on evaluating existing methods rather than introducing new ones.
The study investigated the impact of various data augmentation methods on unsupervised anomaly detection algorithms for industrial images, finding that performance is not significantly affected by specific methods and combining multiple methods does not generally improve accuracy, though it can work well in some cases.
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.