Abstract
Deep Convolutional Neural Network (DCNN) is a kind of multi layer neural network models. In these years, the DCNN is attracting the attention since it shows the state-of-the-arts performance in the image and speech recognition tasks. However, the design for the architecture of the DCNN has not so much discussed since we have not found effective guideline to construct. In this research, we focus on within-class variance of SVM histogram proposed in our previous work [8]. We try to apply it as a clue for modifying the architecture of a DCNN, and confirm the modified DCNN shows better performance than that of the original one.
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Acknowledgment
This work is partly supported by MEXT/JSPS KAKENHI Grant number 26120515 and 16H01542.
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Suzuki, S., Shouno, H. (2016). An Architecture Design Method of Deep Convolutional Neural Network. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9949. Springer, Cham. https://doi.org/10.1007/978-3-319-46675-0_59
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DOI: https://doi.org/10.1007/978-3-319-46675-0_59
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