A Method to Visualize Deep Convolutional Networks Based on Model Reconstruction

Jiaming LIU; Mengdao XING; Jixiang FU; Dan XU

doi:10.11999/JEIT180916

Volume 41 Issue 9

Sep. 2019

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Jiaming LIU, Mengdao XING, Jixiang FU, Dan XU. A Method to Visualize Deep Convolutional Networks Based on Model Reconstruction[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2194-2200. doi: 10.11999/JEIT180916

Citation:

Jiaming LIU, Mengdao XING, Jixiang FU, Dan XU. A Method to Visualize Deep Convolutional Networks Based on Model Reconstruction[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2194-2200. doi: 10.11999/JEIT180916

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A Method to Visualize Deep Convolutional Networks Based on Model Reconstruction

doi: 10.11999/JEIT180916

National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds: The National Defense Science and Technology Excellent Youth Talent Foundation of China (2017-JCJQ-ZQ-061)

Received Date: 2018-09-21
Rev Recd Date: 2019-02-19

Available Online: 2019-03-21

Publish Date: 2019-09-10

Abstract

Abstract

A method for visualizing the weights of a reconstructed model is proposed to analyze a deep convolutional network works. Firstly, a specific input is used in the original neural network during the forward propagation to get the prior information for model reconstruction. Then some of the structure of the original network is changed for further parameter calculation. After that, the parameters of the reconstructed model are calculated with a group of orthogonal vectors. Finally, the parameters are put into a special order to make them visualized. Experimental results show that the model reconstructed with the proposed method is totally equivalent to the original model during the forward propagation in the classification process. The feature of the weights of the reconstructed model can be observed clearly and the principle of the neural network can be analyzed with the feature.
- Deep convolutional networks,
- Visualization,
- Model reconstruction

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References

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