  We introduce a powerful student-teacher framework for the challenging problem
of unsupervised anomaly detection and pixel-precise anomaly segmentation in
high-resolution images. Student networks are trained to regress the output of a
descriptive teacher network that was pretrained on a large dataset of patches
from natural images. This circumvents the need for prior data annotation.
Anomalies are detected when the outputs of the student networks differ from
that of the teacher network. This happens when they fail to generalize outside
the manifold of anomaly-free training data. The intrinsic uncertainty in the
student networks is used as an additional scoring function that indicates
anomalies. We compare our method to a large number of existing deep learning
based methods for unsupervised anomaly detection. Our experiments demonstrate
improvements over state-of-the-art methods on a number of real-world datasets,
including the recently introduced MVTec Anomaly Detection dataset that was
specifically designed to benchmark anomaly segmentation algorithms.
