References
- Hinton G E, Salakhutdinov R R, Reducing the dimensionality of data with neural networks[J].Science, 2006,313(5786):504–507.
- Rumelhart D E, Hinton G E, Williams RJ. Learning representations by back-propagating errors[J].Nature, 1986, 323:533–538.
- Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, november 1998.
- LeCun Y, Cortea C. MNIST handwritten digit database[EB/OL].http://yann.lecun.com/exdb/mnist, 2010.
- K. Simonyan and A. Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition.
- He K, Zhang x, Ren S, et al. Deep residual learning for image recognition[EB/OL].http://arxiv.org/abs/1512.03385,2015.
- Gu Jiu-Xiang, Wang Zhen-Hua, Jason Kuen, et al Recentadvances in convolutional neural networks. arXiv:1512.07180v5,2017.
- X. Glorot, Y. Bengio. Understanding the difficulty of training deep feedforward neural networks. AISTATS, 2010
- Lecun Y, Boser B, Denker J S, et al. Back propagation applied to handwritten zip code recognition[J]. Neural Computation, 1989, 1(4):541–551.
- Krizhevsky A, Sutskever I, Hinton G E. Imagenet classifi-cation with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems, 2012:1097–1105.
- Bengio Y, Simard P, Frasconi P. Learning long-term depen-dencies with gradient descent is difficult[J]. IEEE Transac-tions on Neural Networks, 1994, 5(2):157–166.
- Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, Going Deeper with Convolutions, Arxiv Link: http://arxiv.org/abs/1409.4842.
- Rougui J E, Istrate D, Souidene W, et al. Audio Based Sur-veillance for Cognitive Assitance Using a CMT Microphonewithin Socially Assistive Living [J]. 2009, 2009 (2009):2547–2550.]
- Lin, Min, Qiang Chen, and Shuicheng Yan. Network in network. arXiv preprint arXiv:1312.4400
- Zang D, Chai Z, Zhang J, et al. Vehicle license plate recog-nition using visual attention model and deep learning[J]. Journal of Electronic Imaging, 2015, 24(3):033001.
- Xv Shanshan. Research and application of convolutional neural network [D]. Nanjing forestry university, 2013.
- Hinton G E, Srivastava N, Krizhevsky A, et al Improving neural networks by preventing co-adaption of feature detectors[ Ｒ /OL].[2015-10-26]. http://arxiv.org/pdf/12070580v1
- Wan L, Zeiler M, Zhang S, et al. Regularization of neural networks using drop connect [C] / / Proceedings of the 2013 Inter-national Conference on Machine Learning. New York: ACM Press, 2013: 1058－1066.
- Luo Ke, Zhou Anzhong, Luo Xiao. A Convolutional neural network training strategy using knowledge transfer[J]. Control and decision-making, 2019, 034 (003) : 511-518.
- Cao Ge. Research on face image classification application based on deep convolutional neural network [D]. Jilin university, 2019.
- Shi Wenxu, Jiang Jinhong, Bao Shengli. Application of improved convolutional neural network in pathological image classification of adenocarcinoma[J]. Science, technology and engineering, 209,19 (35) : 279-285.
- Zhang Chun-lei. Military target image classification technology based on parallel convolutional neural network[J]. Electronic design engineering, 209, 27 (08) : 76-80.
- Rao Jianghao. Target identification, positioning and detection based on CNN integration [D]. University of Chinese academy of sciences (institute of optoelectronic technology, Chinese academy of sciences), 2018.
- Li Dajun, He Weilong, Guo Bingxuan, Li Maosen, Chen Minqiang. Building target detection algorithm based on Mask - RCNN[J]. Science of surveying and mapping, 202,44 (10) : 172-180.
- DingPeng. Research on optical remote sensing target detection technology based on deep convolutional neural network [D]. University of Chinese academy of sciences (changchun institute of optics, precision mechanics and physics, cas), 2019.
- Zhang Qi, Ding Xintao, Wang Wanjun, SZhou Wen. Traffic target detection method based on Faster RCNN [J]. Journal of west anhui university, 202,35 (05) : 50-55.