  Anomaly detection and localization are important problems in computer vision.
Recently, Convolutional Neural Network (CNN) has been used for visual
inspection. In particular, the scarcity of anomalous samples increases the
difficulty of this task, and unsupervised leaning based methods are attracting
attention. We focus on Student-Teacher Feature Pyramid Matching (STPM) which
can be trained from only normal images with small number of epochs. Here we
proposed a powerful method which compensates for the shortcomings of STPM.
Proposed method consists of two students and two teachers that a pair of
student-teacher network is the same as STPM. The other student-teacher network
has a role to reconstruct the features of normal products. By reconstructing
the features of normal products from an abnormal image, it is possible to
detect abnormalities with higher accuracy by taking the difference between
them. The new student-teacher network uses attention modules and different
teacher network from the original STPM. Attention mechanism acts to
successfully reconstruct the normal regions in an input image. Different
teacher network prevents looking at the same regions as the original STPM. Six
anomaly maps obtained from the two student-teacher networks are used to
calculate the final anomaly map. Student-teacher network for reconstructing
features improved AUC scores for pixel level and image level in comparison with
the original STPM.
