  Frequent false alarms impede the promotion of unsupervised anomaly detection
algorithms in industrial applications. Potential characteristics of false
alarms depending on the trained detector are revealed by investigating density
probability distributions of prediction scores in the out-of-distribution
anomaly detection tasks. An SVM-based classifier is exploited as a
post-processing module to identify false alarms from the anomaly map at the
object level. Besides, a sample synthesis strategy is devised to incorporate
fuzzy prior knowledge on the specific application in the anomaly-free training
dataset. Experimental results illustrate that the proposed method
comprehensively improves the performances of two segmentation models at both
image and pixel levels on two industrial applications.
