  Anomaly detection is an important problem in computer vision; however, the
scarcity of anomalous samples makes this task difficult. Thus, recent anomaly
detection methods have used only normal images with no abnormal areas for
training. In this work, a powerful anomaly detection method is proposed based
on student-teacher feature pyramid matching (STPM), which consists of a student
and teacher network. Generative models are another approach to anomaly
detection. They reconstruct normal images from an input and compute the
difference between the predicted normal and the input. Unfortunately, STPM does
not have the ability to generate normal images. To improve the accuracy of
STPM, this work uses a student network, as in generative models, to reconstruct
normal features. This improves the accuracy; however, the anomaly maps for
normal images are not clean because STPM does not use anomaly images for
training, which decreases the accuracy of the image-level anomaly detection. To
further improve accuracy, a discriminative network trained with
pseudo-anomalies from anomaly maps is used in our method, which consists of two
pairs of student-teacher networks and a discriminative network. The method
displayed high accuracy on the MVTec anomaly detection dataset.
