  Detecting anomalies using deep learning has become a major challenge over the
last years, and is becoming increasingly promising in several fields. The
introduction of self-supervised learning has greatly helped many methods
including anomaly detection where simple geometric transformation recognition
tasks are used. However these methods do not perform well on fine-grained
problems since they lack finer features. By combining in a multi-task framework
high-scale shape features oriented task with low-scale fine features oriented
task, our method greatly improves fine-grained anomaly detection. It
outperforms state-of-the-art with up to 31% relative error reduction measured
with AUROC on various anomaly detection problems.
