2019年
A convolutional neural network with sign-to-position format conversion
Proceedings of SPIE - The International Society for Optical Engineering
- ,
- ,
- 巻
- 11049
- 号
- 記述言語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1117/12.2521349
© COPYRIGHT SPIE. This paper tries improving image recognition accuracy with Convolutional Neural Networks (CNNs). CNNs are one of state-of-the-art image recognition frameworks, and have used the Rectified Linear Unit (ReLU) as the activation function. However, the ReLU rectifies negative values to zero. This paper applies the Sign-to-Position (S/P) format conversion after convolutional procedures to eliminate the rectification loss. Experimental results show that the proposed method improves the recognition accuracy of the MNIST and Fashion-MNIST data set by 0.50% and 1.30% compared with a conventional CNN respectively. The S/P format conversion also contributes to negative image recognition, and results in 12.58% and 3.66% higher accuracy.
- リンク情報
- ID情報
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- DOI : 10.1117/12.2521349
- ISSN : 0277-786X
- eISSN : 1996-756X
- SCOPUS ID : 85063871546