论文标题
Expdnn：可解释的深神经网络
ExpDNN: Explainable Deep Neural Network
论文作者
论文摘要
近年来，已经应用了深层神经网络来获得预测，分类和模式识别的高性能。但是，这些深度神经网络中的权重很难解释。尽管线性回归方法可以提供可解释的结果，但在输入相互作用的情况下，该方法不合适。因此，提出了具有可解释层的可解释的深层神经网络（EXPDNN），以在输入相互作用的情况下获得可解释的结果。给出了三种情况以评估所提出的expdnn，结果表明，可解释层中重量的绝对值可用于解释相应的输入的重量以提取特征提取。
In recent years, deep neural networks have been applied to obtain high performance of prediction, classification, and pattern recognition. However, the weights in these deep neural networks are difficult to be explained. Although a linear regression method can provide explainable results, the method is not suitable in the case of input interaction. Therefore, an explainable deep neural network (ExpDNN) with explainable layers is proposed to obtain explainable results in the case of input interaction. Three cases were given to evaluate the proposed ExpDNN, and the results showed that the absolute value of weight in an explainable layer can be used to explain the weight of corresponding input for feature extraction.
