[1]
|
袁培森, 黎薇, 任守纲, 等. 基于卷积神经网络的菊花花型和品种识别[J]. 农业工程学报, 2018, 34(5): 152-158.
|
[2]
|
傅隆生, 冯亚利, Elkamil, 等. 基于卷积神经网络的田间多簇猕猴桃图像识别方法[J]. 农业工程学报, 2018, 34(2): 205-211.
|
[3]
|
佘鹏, 甘健侯, 文斌, 等. 经典深度卷积神经网络模型在手绘草图识别中的应用研究[J]. 云南师范大学学报(自然科学版), 2018(1): 29-34.
|
[4]
|
顾婷婷, 赵海涛, 孙韶媛. 基于金字塔型残差神经网络的红外图像深度估计[J]. 红外技术, 2018(5).
|
[5]
|
Hinton, G.E. and Salakhutdinov, R.R. (2006) Reducing the Dimensionality of Data with Neural Networks. Science, 313, 504-507. https://doi.org/10.1126/science.1127647
|
[6]
|
Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012) ImageNet Classification with Deep Convolutional Neural Networks. International Conference on Neural Information Processing Systems, Curran Associates Inc., 1097-1105.
|
[7]
|
Simonyan, K. and Zisserman, A. (2014) Very Deep Convolutional Networks for Large-Scale Image Recognition. Computer Science.
|
[8]
|
Szegedy, C., Liu, W., Jia, Y., et al. (2015) Going Deeper with Convolutions. IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 1-9.
|
[9]
|
He, K., Zhang, X., Ren, S., et al. (2015) Deep Residual Learning for Image Recognition. 770-778.
|
[10]
|
Lecun, Y., Bottou, L., Bengio, Y., et al. (1998) Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86, 2278-2324. https://doi.org/10.1109/5.726791
|
[11]
|
周飞燕, 金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报, 2017, 40(6): 1229-1251.
|
[12]
|
王忠民, 王希, 宋辉. 基于随机Dropout深度信念网络的移动用户行为识别方法[J]. 计算机应用研究, 2017, 34(12): 3797-3800.
|