Attribution methods calculate attributions that visually explain the predictions of deep neural networks (DNNs) by highlighting important parts of the input features. In particular, gradient-based attribution (GBA) methods are widely used because they can be easily implemented through automatic differentiation. In this study, we use the attributions that filter out irrelevant parts of the input features and then verify the effectiveness of this approach by measuring the classification accuracy of a pre-trained DNN. This is achieved by calculating and applying an \textit{attribution mask} to the input features and subsequently introducing the masked features to the DNN, for which the mask is designed to recursively focus attention on the parts of the input related to the target label. The accuracy is enhanced under a certain condition, i.e., \textit{no implicit bias}, which can be derived based on our theoretical insight into compressing the DNN into a single-layer neural network. We also provide Gradient\,*\,Sign-of-Input (GxSI) to obtain the attribution mask that further improves the accuracy. As an example, on CIFAR-10 that is modified using the attribution mask obtained from GxSI, we achieve the accuracy ranging from 99.8\% to 99.9\% without additional training.
Attribution Explanations for Deep Neural Networks: A Theoretical Perspective
School of Computer Science and Technology, Xi’an Jiaotong University（西安交通大学计算机科学与技术学院）；School of Cyber Science and Engineering, Xi’an Jiaotong University（西安交通大学网络安全工程学院）；Department of Computer Science and Engineering, the John Hopcroft Center, Shanghai Jiao Tong University（上海交通大学计算机科学与工程系、霍夫曼中心）；Department of Data Science, New Jersey Institute of Technology（新泽西理工学院数据科学系）
Sparse Explanations of Neural Networks Using Pruned Layer-Wise Relevance Propagation
Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Germany（哈索普兰特纳研究所，数字工程学院，波茨坦大学，德国）；Scientific Computing Center, Karlsruhe Institute of Technology, Germany（科学计算中心，卡尔斯鲁厄技术大学，德国）
