  In this article, the concepts of transfer and continual learning are
introduced. The ensuing review reveals promising approaches for industrial deep
transfer learning, utilizing methods of both classes of algorithms. In the
field of computer vision, it is already state-of-the-art. In others, e.g. fault
prediction, it is barely starting. However, over all fields, the abstract
differentiation between continual and transfer learning is not benefitting
their practical use. In contrast, both should be brought together to create
robust learning algorithms fulfilling the industrial automation sector's
requirements. To better describe these requirements, base use cases of
industrial transfer learning are introduced.
