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SSD Net: toward deep network models based on dissimilarity metrics
Author(s): Satoshi Arai; Tomoharu Nagao
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Paper Abstract

Most artificial neural networks that originate from the perceptron use the inner product as the basic operation to calculate pattern similarities. Unlike them, we propose a novel hierarchical network model based on a pattern dissimilarity operation using a popular dissimilarity metric: sum of squared differences. Our model is differentiable and end-to-end trainable. We provide a description of the basic formulation and network architecture of the proposed method. Then we apply our method to image classification tasks using public datasets for performance comparison. Although our method does not outperform the same size of convolutional neural network in terms of classification accuracy, it demonstrates that comparable performance can be obtained.

Paper Details

Date Published: 22 March 2019
PDF: 6 pages
Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 110492A (22 March 2019); doi: 10.1117/12.2523764
Show Author Affiliations
Satoshi Arai, Yokohama National Univ. (Japan)
Tomoharu Nagao, Yokohama National Univ. (Japan)


Published in SPIE Proceedings Vol. 11049:
International Workshop on Advanced Image Technology (IWAIT) 2019
Qian Kemao; Kazuya Hayase; Phooi Yee Lau; Wen-Nung Lie; Yung-Lyul Lee; Sanun Srisuk; Lu Yu, Editor(s)

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