Abstract—The rapid progress of convolutional neural
networks (CNNs) in multiple applications of practical
implementation is generally hindered by an upsurge in network
size and computational complexity. Currently, engineers focus
on reducing these problems through compressing the CNNs by
pruning filters and their weights. In this paper, we present a
fresh and easy-to-use pruning approach that reduces the model
size by eliminating complete filters and filter weights based on
the sparse group LASSO (Least Absolute Shrinkage and
Selection Operator) method across the convolutional layers.
More precisely, it regulates the sparsity at the feature level and
the group level. During the process of pruning, the unnecessary
filters with their weights eliminate directly without sacrificing
accuracy in the test, resulting in much compact and slimmer
architectures. We experimentally compute the effectiveness of
our methodology with various state-of-art CNN models on
various benchmark data sets. Mainly, CIFAR-10 data sets
applied on VGG-16 model and reduce the parameters approx.
96.1% and saved approx. 83.55% float-point-operations
(FLOPs) without sacrificing accuracy and have obtained
development in state-of-art.
Index Terms—Convolutional neural networks, filter pruning,
FLOPs, sparse group LASSO.
Aakash Kumar and Baoqun Yin are with the University of Science and
Technology of China, Hefei 2300026, P.R. China (e-mail:
akb@mail.ustc.edu.cn, bqyin@ustc.edu.cn).
Ajeet Kumar Bhatia and Avinash Rohra are with Nanjing University of
Aeronautics and Astronautics, Nanjing, P.R. China (e-mail:
ajeet@nuaa.edu.cn, avinashrohra5@gmail.com).
Aneel Kumar Bhatia is with the University of Sindh, Jamshoro, Pakistan
(e-mail: akumar.bhatia@usindh.edu.pk).
Cite: Aakash Kumar, Baoqun Yin, Ajeet Kumar Bhatia, Aneel Kumar Bhatia, and Avinash Rohra, "Structure Level Pruning of Efficient Convolutional Neural Networks with Sparse Group LASSO," International Journal of Machine Learning and Computing vol. 12, no. 5, pp. 272-278, 2022.
Copyright @ 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).