Abstract
Plant is of vital importance to human life, so it is necessary to research and protect them. Recently, convolutional neural network has been the most widely used technique for the task of image classification, and all kinds of convolutional neural network architecture has been proposed, including Densely connected convolutional networks (DenseNet). It has been shown that the selection of activation functions is of great importance to the training dynamics and task accuracy in deep neural networks. In this paper, we propose a new architecture that combines DenseNet with a new self-gated activation function. The experiment shows that the new architecture can get good results on the task of leaf classification.
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Acknowledgements
This work was supported by the grants of the National Science Foundation of China, Nos. 61472280, 61672203, 61472173, 61572447, 61772357, 31571364, 61520106006, 61772370, 61702371 and 61672382, China Post-doctoral Science Foundation Grant, Nos. 2016M601646 & 2017M611619, and supported by “BAGUI Scholar” Program of Guangxi Zhuang Autonomous Region of China.
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Li, D., Yang, H., Yuan, CA., Qin, X. (2018). Leaf Classification Utilizing Densely Connected Convolutional Networks with a Self-gated Activation Function. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_40
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