Deep learning technology has become one of the core driving forces to promote the in-depth development of industrial automation. In [A1], Wang et al. interpreted the decision process of the convolutional neural network (CNN) by constructing a percolation model from a statistical physics perspective. In this perspective, the decision-making basis of CNN is difficult to understand, because CNN is usually used as a black box model. Furthermore, a novel concept of the differentiation degree and summarized an empirical formula for quantifying the differentiation degree is presented and discussed.

中文翻译：

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客座社论：工业应用中基于科学和物理的机器学习


深度学习技术已成为推动工业自动化深入发展的核心驱动力之一。在[A1]中，Wang 等人。通过从统计物理角度构建渗滤模型来解释卷积神经网络（CNN）的决策过程。从这个角度来看，CNN的决策基础很难理解，因为CNN通常被用作黑盒模型。此外，提出并讨论了分化程度的新概念，并总结了量化分化程度的经验公式。