Abstract
Deep neural network has been successfully used in various fields, and it has received significant results in some typical tasks, especially in computer vision. However, deep neural network are usually trained by using gradient descent based algorithm, which results in gradient vanishing and gradient explosion problems. And it requires expert level professional knowledge to design the structure of the deep neural network and find the optimal hyper parameters for a given task. Consequently, training a deep neural network becomes a very time consuming problem. To overcome the shortcomings mentioned above, we present a model which combining Gabor filter and pseudoinverse learning autoencoders. The method referred in model optimization is a non-gradient descent algorithm. Besides, we presented the empirical formula to set the number of hidden neurons and the number of hidden layers in the entire training process. The experimental results show that our model is better than existing benchmark methods in speed, at same time it has the comparative recognition accuracy also.
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Acknowledgements
The research work described in this paper was fully supported by the grants from the National Natural Science Foundation of China (Project No. 61472043), the Joint Research Fund in Astronomy (U1531242) under cooperative agreement between the NSFC and CAS, and Natural Science Foundation of Shandong (ZR2015FL006). Prof. Ping Guo and Qian Yin are the authors to whom all correspondence should be addressed.
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Deng, X., Feng, S., Guo, P., Yin, Q. (2018). Fast Image Recognition with Gabor Filter and Pseudoinverse Learning AutoEncoders. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11306. Springer, Cham. https://doi.org/10.1007/978-3-030-04224-0_43
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DOI: https://doi.org/10.1007/978-3-030-04224-0_43
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