A deep learning model compression algorithm based on optimal clustering

Qirui Wu; Wenzhen Li; Xu Lu; Hailong Zhang; Hanwu Luo; Cheng Lei

doi:10.1117/12.2524450

6 May 2019 A deep learning model compression algorithm based on optimal clustering

Qirui Wu, Wenzhen Li, Xu Lu, Hailong Zhang, Hanwu Luo, Cheng Lei

Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 1106904 (2019) https://doi.org/10.1117/12.2524450
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China

Abstract

Nowadays, the models of deep learning are increasingly used in various industrial applications. However, the storage space of the models is too large, which makes it quite difficult to apply to mobile devices. In order to solve this problem, a model compression algorithm based on optimal clustering is proposed in this paper. Firstly, the model parameters of fully connected layer in deep convolutional neural network are clustered according to the best clustering method. Then the cluster center of the parameters is selected as the representative of the original parameter matrix. At last, the parameters of the cluster center are transformed differently in the forward calculation of the model to achieve the effect of compressing the parameters of the model and ensured the accuracy of the model. The compression algorithm proposed here is compared with other model compression algorithms in several common deep learning models such as Alexnet, VGG16 and so on. The results show that the algorithm proposed in this paper can compress the memory of the model greatly and improve the accuracy.

Citation Download Citation

Qirui Wu, Wenzhen Li, Xu Lu, Hailong Zhang, Hanwu Luo, and Cheng Lei "A deep learning model compression algorithm based on optimal clustering", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 1106904 (6 May 2019); https://doi.org/10.1117/12.2524450

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KEYWORDS

Data modeling

Databases

Statistical modeling

Image compression

Data centers

Distortion

Convolution

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