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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

A Method to Visualize Deep Convolutional Networks Based on Model Reconstruction

doi: 10.11999/JEIT180916
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
  • 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.
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  • FARAHBAKHSH E, KOZEGAR E, and SORYANI M. Improving Persian digit recognition by combining data augmentation and AlexNet[C]. Iranian Conference on Machine Vision and Image Processing, Isfahan, Iran, 2017: 265–270.
    HOU Saihui, LIU Xu, and WANG Zilei. DualNet: Learn complementary features for image recognition[C]. 2017 IEEE International Conference on Computer Vision, Venice, Italy, 2017: 502–510.
    SZEGEDY C, LIU Wei, JIA Yangqing, et al.. Going deeper with convolutions[C]. 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 1–9.
    HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al.. Deep residual learning for image recognition[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770–778.
    王俊, 郑彤, 雷鹏, 等. 深度学习在雷达中的研究综述[J]. 雷达学报, 2018, 7(4): 395–411. doi: 10.12000/JR18040

    WANG Jun, ZHENG Tong, LEI Peng, et al. Study on deep learning in radar[J]. Journal of Radars, 2018, 7(4): 395–411. doi: 10.12000/JR18040
    PUNJABI A and KATSAGGELOS A K. Visualization of feature evolution during convolutional neural network training[C]. The 25th European Signal Processing Conference, Kos, Greece, 2017: 311–315.
    ZEILER M D and FERGUS R. Visualizing and understanding convolutional networks[C]. The 13th European Conference on Computer Vision, Zurich, Switzerland, 2014: 818–833.
    ZHOU Bolei, KHOSLA A, LAPEDRIZA A, et al.. Learning deep features for discriminative localization[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 2921–2929.
    SUZUKI S and SHOUNO H. A study on visual interpretation of network in network[C]. 2017 International Joint Conference on Neural Networks, Anchorage, USA, 2017: 903–910.
    GAL Y and GHAHRAMANI Z. Dropout as a Bayesian approximation: Representing model uncertainty in deep learning[C]. The 33rd International Conference on Machine Learning, New York, USA, 2016: 1050–1059.
    NAIR V and HINTON G E. Rectified linear units improve restricted Boltzmann machines[C]. The 27th International Conference on International Conference on Machine Learning, Haifa, Israel, 2010: 807–814.
    PEHLEVAN C and CHKLOVSKII D B. A normative theory of adaptive dimensionality reduction in neural networks[C]. The 28th International Conference on Neural Information Processing Systems, Montreal, Canada, 2015: 2269–2277.
    IOFFE S and SZEGEDY C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[C]. The 32nd International Conference on International Conference on Machine Learning, Lille, France, 2015: 448–456.
    王思雨, 高鑫, 孙皓, 等. 基于卷积神经网络的高分辨率SAR图像飞机目标检测方法[J]. 雷达学报, 2017, 6(2): 195–203. doi: 10.12000/JR17009

    WANG Siyu, GAO Xin, SUN Hao, et al. An aircraft detection method based on convolutional neural networks in high-resolution SAR images[J]. Journal of Radars, 2017, 6(2): 195–203. doi: 10.12000/JR17009
    NOH H, HONG S, and HAN B. Learning deconvolution network for semantic segmentation[C]. 2015 IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 1520–1528.
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