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On the use of variable stride in convolutional neural networks

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Abstract

This paper explores the idea of changing the stride value in convolutional neural networks depending on the position of the pixel within the image: a smaller stride value is used when processing the center of the image, while a larger one is used for pixels close to the edges. We show several examples of image classification tasks where the proposed approach outperforms a baseline solution of same computational cost using fixed stride and several counterexamples where it does not – and explain why this is so. The proposed method has been successfully tested using several contemporary datasets and can be easily implemented and extended to other image classification tasks.

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Notes

  1. In our experiments the same basic idea was adapted and implemented with different image sizes, depending on the dataset used.

  2. The actual values will, of course, change depending on the size of the input image for each dataset/experiment in Section 4.

  3. This is the layer in which we modify the stride parameter.

  4. The number of nodes in this layer will vary depending on the experiment: experiments 1 and 2 involve 8-class classifiers whereas experiments 3 through 5 consist of 10-class classifiers.

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  1. Florida Atlantic University, 777 Glades Rd, Boca Raton, FL, 33431, USA

    Luiz Zaniolo & Oge Marques

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  1. Luiz Zaniolo
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  2. Oge Marques
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Correspondence to Oge Marques.

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Zaniolo, L., Marques, O. On the use of variable stride in convolutional neural networks. Multimed Tools Appl 79, 13581–13598 (2020). https://doi.org/10.1007/s11042-019-08385-4

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