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Nonconvex Regularization for Network Slimming: Compressing CNNs Even More

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Advances in Visual Computing (ISVC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12509))

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Abstract

In the last decade, convolutional neural networks (CNNs) have evolved to become the dominant models for various computer vision tasks, but they cannot be deployed in low-memory devices due to its high memory requirement and computational cost. One popular, straightforward approach to compressing CNNs is network slimming, which imposes an \(\ell _1\) penalty on the channel-associated scaling factors in the batch normalization layers during training. In this way, channels with low scaling factors are identified to be insignificant and are pruned in the models. In this paper, we propose replacing the \(\ell _1\) penalty with the \(\ell _p\) and transformed \(\ell _1\) (T\(\ell _1\)) penalties since these nonconvex penalties outperformed \(\ell _1\) in yielding sparser satisfactory solutions in various compressed sensing problems. In our numerical experiments, we demonstrate network slimming with \(\ell _p\) and T\(\ell _1\) penalties on VGGNet and Densenet trained on CIFAR 10/100. The results demonstrate that the nonconvex penalties compress CNNs better than \(\ell _1\). In addition, T\(\ell _1\) preserves the model accuracy after channel pruning, and \(\ell _{1/2, 3/4}\) yield compressed models with similar accuracies as \(\ell _1\) after retraining.

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Acknowledgments

The work was partially supported by NSF grants IIS-1632935, DMS-1854434, DMS-1952644, and a Qualcomm Faculty Award. The authors thank Mingjie Sun for having the code for [31] available on GitHub.

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Authors and Affiliations

  1. Department of Mathematics, University of California, Irvine, Irvine, CA, 92697, USA

    Kevin Bui, Shuai Zhang, Yingyong Qi & Jack Xin

  2. Department of Mathematics and Computer Science, Whittier College, Whittier, 90602, CA, USA

    Fredrick Park

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  1. Kevin Bui
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Correspondence to Jack Xin .

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Editors and Affiliations

  1. University of Nevada Reno, Reno, NV, USA

    George Bebis

  2. Stony Brook University, Stony Brook, NY, USA

    Zhaozheng Yin

  3. Drexel University, Philadelphia, PA, USA

    Edward Kim

  4. RWTH Aachen University, Aachen, Germany

    Jan Bender

  5. University of Edinburgh, Edinburgh, UK

    Kartic Subr

  6. IBM Research – Cambridge, Cambridge, MA, USA

    Bum Chul Kwon

  7. University of Waterloo, Waterloo, ON, Canada

    Jian Zhao

  8. Graz University of Technology, Graz, Austria

    Denis Kalkofen

  9. The Hong Kong Polytechnic University, Hong Kong, Hong Kong

    George Baciu

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Bui, K., Park, F., Zhang, S., Qi, Y., Xin, J. (2020). Nonconvex Regularization for Network Slimming: Compressing CNNs Even More. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science(), vol 12509. Springer, Cham. https://doi.org/10.1007/978-3-030-64556-4_4

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  • DOI: https://doi.org/10.1007/978-3-030-64556-4_4

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