  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.
