An Efficient Binary Search Based Neuron Pruning Method for ConvNet Condensation

  • Boyu ZhangEmail author
  • A. K. Qin
  • Jeffrey Chan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10635)


Convolutional neural networks (CNNs) have been widely applied in the field of computer vision. Nowadays, the architecture of CNNs is becoming more and more complex, involving more layers and more neurons per layer. The augmented depth and width of CNNs will lead to greatly increased computational and memory costs, which may limit CNNs practical utility. However, as demonstrated in previous research, CNNs of complex architecture may contain considerable redundancy in terms of hidden neurons. In this work, we propose a magnitude based binary neuron pruning method which can selectively prune neurons to shrink the network size while keeping the performance of the original model without pruning. Compared to some existing neuron pruning methods, the proposed method can achieve higher compression rate while automatically determining the number of neurons to be pruned per hidden layer in an efficient way.


Deep learning Convolutional neural network Condensation Pruning 



This research is supported by Chinese Scholarship Council.


  1. 1.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  2. 2.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)Google Scholar
  3. 3.
    Denil, M., Shakibi, B., Dinh, L., Ranzato, M., de Freitas, N.: Predicting parameters in deep learning. In: Nips, pp. 2148–2156 (2013)Google Scholar
  4. 4.
    Ba, L., Caurana, R.: Do deep nets really need to be deep? In: Advances in Neural Information Processing Systems 2014, pp. 1–6 (2014)Google Scholar
  5. 5.
    Chen, W., Wilson, J.T., Tyree, S., Weinberger, K.Q., Chen, Y.: Compressing Convolutional Neural Networks. arXiv:1506.04449, pp. 1–9 (2015)
  6. 6.
    Chen, W., Wilson, J.T., Tyree, S., Weinberger, K.Q., Chen, Y.: Compressing neural networks with the hashing trick. CoRR, abs/1504.04788 (2015)Google Scholar
  7. 7.
    Han, S., Mao, H., Dally, W.J.: Deep compression - compressing deep neural networks with pruning, trained quantization and Huffman coding. In: Iclr, pp. 1–13 (2016)Google Scholar
  8. 8.
    Han, S., Pool, J., Tran, J., Dally, W.J.: Learning both weights and connections for efficient neural networks. In: Nips, pp. 1135–1143 (2015)Google Scholar
  9. 9.
    Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. In: NIPS 2014 Deep Learning Workshop, pp. 1–9 (2015)Google Scholar
  10. 10.
    Hu, H., Peng, R., Tai, Y.W., Tang, C.K.: Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures (2016)Google Scholar
  11. 11.
    Jaderberg, M., Vedaldi, A., Zisserman, A.: Speeding up Convolutional Neural Networks with Low Rank Expansions. arXiv preprint arXiv:1405.3866, p. 7 (2014)
  12. 12.
    Lebedev, V., Ganin, Y., Rakhuba1, M., Oseledets, I., Lempitsky, V.: Speeding-up convolutional neural networks using fine-tuned CP-Decomposition. In: Iclr, pp. 1–10 (2015)Google Scholar
  13. 13.
    Li, H., Kadav, A., Durdanovic, I., Samet, H., Graf, H.P.: Pruning Filters for Efficient ConvNets (2016)Google Scholar
  14. 14.
    Mariet, Z., Sra, S.: Diversity networks. In: Iclr, pp. 1–11 (2015)Google Scholar
  15. 15.
    Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: FitNets: Hints for Thin Deep Nets, pp. 1–13 (2014)Google Scholar
  16. 16.
    Srinivas, S., Babu, R.V., Education, S.: Data-free Parameter Pruning for Deep Neural Networks, pp. 1–12 (2015)Google Scholar
  17. 17.
    Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)Google Scholar
  18. 18.
    He, T., Fan, Y., Qian, Y., Tan, T., Yu, K.: Reshaping deep neural network for fast decoding by node-pruning. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 245–249. IEEE (2014)Google Scholar
  19. 19.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  20. 20.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  21. 21.
    Griffin, G., Holub, A., Perona, P.: Caltech-256 Object Category Dataset (2007)Google Scholar
  22. 22.
    Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., Xiao, T., Xu, B., Zhang, C., Zhang, Z.: Mxnet: a flexible and efficient machine learning library for heterogeneous distributed systems. arXiv preprint arXiv:1512.01274 (2015)

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.RMIT UniversityMelbourneAustralia
  2. 2.Swinburne University of TechnologyMelbourneAustralia

Personalised recommendations