Deep learning based on a large number of high-quality data plays an important role in many industries. However, deep learning is hard to directly embed in the real-time system, because the data accumulation of the system depends on real-time acquisitions. However, the analysis tasks of such systems need to be carried out in real time, which makes it impossible to complete the analysis tasks by accumulating data for a long time. In order to solve the problems of high-quality data accumulation, high timeliness of the data analysis, and difficulty in embedding deep-learning algorithms directly in real-time systems, this paper proposes a new progressive deep-learning framework and conducts experiments on image recognition. The experimental results show that the proposed framework is effective and performs well and can reach a conclusion similar to the deep-learning framework based on large-scale data.

中文翻译：

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渐进系统：用于工业生产中实时数据的深度学习框架

基于大量高质量数据的深度学习在许多行业中发挥着重要作用。但是，深度学习很难直接嵌入到实时系统中，因为系统的数据积累取决于实时获取。然而，这种系统的分析任务需要实时执行，这使得不可能通过长时间累积数据来完成分析任务。为了解决高质量的数据积累，数据分析的及时性以及将深度学习算法直接直接嵌入实时系统中的难题，本文提出了一种新的渐进式深度学习框架，并对图像进行了实验。承认。