Using logic rules to achieve interpretable Convolutional Neural Network
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Abstract
Using logic rules in the Convolutional Neural Network (CNN) is helpful of CNN. The motivation of our paper is to show that there is a possibility to turn black box to white box. Moreover, in the proposed methodology, the output and output production (convolutional formula) will be interpretable. In our paper, it is shown that it would be possible to go from LCNN to CNN and vice versa. For this reason, score function is developed using quantum logic formula. Therefore, it is proven that there are some rules between input and output and the way of output production could be interpreted by the rules. This rules help us to understand CNN method.
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