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A Survey for Sparse Regularization Based Compression Methods

Author

Listed:
  • Anda Tang

    (University of Chinese Academy of Sciences)

  • Pei Quan

    (University of Chinese Academy of Sciences)

  • Lingfeng Niu

    (University of Chinese Academy of Sciences)

  • Yong Shi

    (Chinese Academy of Sciences)

Abstract

In recent years, deep neural networks (DNNs) have attracted extensive attention due to their excellent performance in many fields of vision and speech recognition. With the increasing scale of tasks to be solved, the network used is becoming wider and deeper, which requires millions or even billions of parameters. The deep and wide network with many parameters brings the problems of memory requirement, computing overhead and over fitting, which seriously hinder the application of DNNs in practice. Therefore, a natural idea is to train sparse networks and floating-point operators with fewer parameters while maintaining considerable performance. In the past few years, people have done a lot of research in the field of neural network compression, including sparse-inducing methods, quantization, knowledge distillation and so on. And the sparse-inducing methods can be roughly divided into pruning, dropout and sparse regularization based optimization. In this paper, we briefly review and analyze the sparse regularization optimization methods. For the model and optimization method of sparse regularization based compression, we discuss both the different advantages and disadvantages. Finally, we provide some insights and discussions on how to make sparse regularization fit within the compression framework.

Suggested Citation

  • Anda Tang & Pei Quan & Lingfeng Niu & Yong Shi, 2022. "A Survey for Sparse Regularization Based Compression Methods," Annals of Data Science, Springer, vol. 9(4), pages 695-722, August.
    • Handle: RePEc:spr:aodasc:v:9:y:2022:i:4:d:10.1007_s40745-022-00389-6
    DOI: 10.1007/s40745-022-00389-6
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    References listed on IDEAS

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