  Industrial defect detection is commonly addressed with anomaly detection (AD)
methods where no or only incomplete data of potentially occurring defects is
available. This work discovers previously unknown problems of student-teacher
approaches for AD and proposes a solution, where two neural networks are
trained to produce the same output for the defect-free training examples. The
core assumption of student-teacher networks is that the distance between the
outputs of both networks is larger for anomalies since they are absent in
training. However, previous methods suffer from the similarity of student and
teacher architecture, such that the distance is undesirably small for
anomalies. For this reason, we propose asymmetric student-teacher networks
(AST). We train a normalizing flow for density estimation as a teacher and a
conventional feed-forward network as a student to trigger large distances for
anomalies: The bijectivity of the normalizing flow enforces a divergence of
teacher outputs for anomalies compared to normal data. Outside the training
distribution the student cannot imitate this divergence due to its
fundamentally different architecture. Our AST network compensates for wrongly
estimated likelihoods by a normalizing flow, which was alternatively used for
anomaly detection in previous work. We show that our method produces
state-of-the-art results on the two currently most relevant defect detection
datasets MVTec AD and MVTec 3D-AD regarding image-level anomaly detection on
RGB and 3D data.
