论文标题
工业自动化的深度转移学习：对数据驱动机器学习的新技术的审查和讨论
Deep Transfer Learning for Industrial Automation: A Review and Discussion of New Techniques for Data-Driven Machine Learning
论文作者
论文摘要
在本文中，引入了转移和持续学习的概念。随后的审查揭示了利用两种算法的方法，可以利用工业深度转移学习的有前途的方法。在计算机视觉领域，它已经是最新的。在其他情况下，例如故障预测，它几乎没有启动。但是，在所有领域中，连续学习和转移学习之间的抽象区别并不能使他们的实际使用受益。相比之下，两者都应聚集在一起，以创建满足工业自动化部门要求的强大学习算法。为了更好地描述这些要求，引入了工业转移学习的基本用例。
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.
