Convolutional neural networks (CNNs) have exhibited superior performance in various types of classification and prediction tasks, but their interpretability remains to be low despite years of research effort. It is crucial to improve the ability of existing models to interpret deep neural networks from both theoretical and practical perspectives and to develop new neural network models with interpretable representations. The aim of this paper is to propose a set of novel masked CNN (MCNN) models with better ability to interpret networks and more accurate prediction. The key ideas behind MCNNs are to introduce a latent binary network to extract informative regions of interest that contain important signals for prediction and to integrate the latent binary network with CNNs to achieve better prediction in various supervised learning problems. Extensive numerical studies demonstrate the competitive performance of the proposed MCNN models.

中文翻译：

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掩码卷积神经网络用于监督学习问题

卷积神经网络（CNN）在各种类型的分类和预测任务中均表现出卓越的性能，但是尽管经过多年的研究，它们的可解释性仍然很低。至关重要的是，要提高现有模型从理论和实践角度解释深度神经网络的能力，并开发具有可解释表示形式的新神经网络模型。本文的目的是提出一组新颖的，具有更好的网络解释能力和更准确的预测能力的蒙版CNN（MCNN）模型。MCNN背后的关键思想是引入一个潜在的二值网络，以提取包含重要预测信号的感兴趣的信息区域，并将该潜在的二值网络与CNN集成，以在各种监督学习问题中实现更好的预测。