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Local channel transformation for efficient convolutional neural network

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Abstract

The efficiency of the convolutional neural network (CNN) model is one of the biggest limitations when CNN is applied to mobile devices. As an efficient convolution method, the pointwise convolution is widely used in many networks to achieve the expansion and compression of channel dimensions. Nevertheless, pointwise convolution still needs to consume a vast number of calculations and parameters. In this paper, depthwise channel ascent (DCA) and group channel descent (GCD) were introduced as efficient channel transformation methods to replace pointwise convolution. DCA and GCD decompose the global channel transformation into local channel transformation. DCA utilizes spatial features to expand channel dimension, while GCD utilizes non-learning channel compression to decrease channel dimension. Compared with other counterparts, networks equipped with DCA and GCD can significantly reduce the calculations and parameters while maintaining competitive accuracy. The performance of the proposed method had been verified in different networks and multiple datasets. In the DCASE2019 dataset, the proposed method can reduce the parameters and calculation of MobileNetV2 to 41.9% and 30.2% and accelerate the inference speed to 73.8%.

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Acknowledgements

The authors acknowledge the financial support from the National Natural Science Fund of China (Grant No. 62001323).

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Authors and Affiliations

  1. Tianjin University, Tianjin, China

    Tao Zhang, Shuang Li, Guoqing Feng, Jinhua Liang, Lun He & Xin Zhao

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  1. Tao Zhang
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  2. Shuang Li
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  3. Guoqing Feng
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  4. Jinhua Liang
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  5. Lun He
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  6. Xin Zhao
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Correspondence to Jinhua Liang.

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Zhang, T., Li, S., Feng, G. et al. Local channel transformation for efficient convolutional neural network. SIViP 17, 129–137 (2023). https://doi.org/10.1007/s11760-022-02212-4

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