Deep Transfer Learning for Industrial Automation: A Review and Discussion of New Techniques for Data-Driven Machine Learning

• URL: http://arxiv.org/abs/2012.03301v1
• Date: Sun, 6 Dec 2020 15:58:22 GMT
• Title: Deep Transfer Learning for Industrial Automation: A Review and Discussion of New Techniques for Data-Driven Machine Learning
• Authors: Benjamin Maschler and Michael Weyrich
• Abstract summary: In this article, the concepts of transfer and continual learning are introduced. The article reveals promising approaches for industrial deep transfer learning, utilizing methods of both classes of algorithms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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