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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… Click to show full abstract
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.
Journal Title: IEEE Transactions on Industrial Informatics
Year Published: 2023
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