Focusing on the interpretability problems of image classification models based on deep convolutional neural network

a visualization method for improving the feature space of model is proposed by evaluating the potential expressiveness of model feature space. Given any pre-trained deep model

firstly the method generates an image by the normalized operation of the gradient in the back propagation

which maximizes activation the class score

and then uses the momentum of the stochastic gradient descent training strategy for back propagation to the original input image. The conventional regularization technique cannot adjust the feature space of the model. Therefore

the spatial pyramid decomposition method is proposed on the basis of the existing regularization method. By constructing the multi-layer Laplacian spatial pyramid

the low frequency component of the target image feature space is promoted

combined with multi-layer Gaussian spatial pyramid to adjust the high-frequency components of its feature space to obtain a better visualization effect. By limiting the region of visualization

it is proposed to use the class activation map to suppress the context-free information

which can further improve the visualization effect. The visualization experiments are performed on the different classes of the model and the individual neurons of the convolution layer. Results show that the proposed method can achieve better visualization effect in different depth models and different visualization tasks.