Masked convolutional neural network for supervised learning problems
Masked convolutional neural network for supervised learning problems
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作者：
Liu，Leo Yu-Feng;Liu，Yufeng;Zhu，Hongtu
卷积神经网络（CNN）在各种类型的分类和预测任务中表现出了上级性能，但尽管经过多年的研究，其可解释性仍然很低。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的新神经网络模型至关重要。本文的目的是提出一组新的掩蔽CNN（MCNN）模型，具有更好的解释网络和更准确预测的能力。MCNN背后的关键思想是引入潜在二进制网络来提取包含重要预测信号的信息区域，并将潜在二进制网络与CNN集成，以在各种监督学习问题中实现更好的预测。大量的数值研究表明，所提出的MCNN模型的竞争力的表现。
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.