[1] Serma P, LeCun Y. Traffic sign recognition with multiscale convolutional wks [C]The 2011 International Joint Conference on Neural wks. IEEE, 2011: 28092813.
[2] A Andreopoulos, J K Tsotsos. 50 years of object recognition: Directions forward. Computer Vision and Image Understanding, 117, 827-891(2013).
[3] M P Edgar, G M Gibson, M J Padgett. Principles and prospects for single-pixel imaging. Nature Photonics, 13, 13-20(2019).
[4] Z Zhang, X Ma, J Zhong. Single-pixel imaging by means of Fourier spectrum acquisition. Nature Communications, 6, 6225(2015).
[5] G M Gibson, S D Johnson, M J Padgett. Single-pixel imaging 12 years on: a review. Optics Express, 28, 28190-28208(2020).
[6] B Sun, M P Edgar, R Bowman, et al. 3 D computational imaging with single-pixel detectors. Science, 340, 844-847(2013).
[7] M J Sun, J M Zhang. Single-pixel imaging and its application in three-dimensional reconstruction: A brief review. Sensors, 19, 732(2019).
[8] M Yao, Z Cai, X Qiu, et al. Full-color light-field microscopy via single-pixel imaging. Optics Express, 28, 6521-6536(2020).
[9] P Latorre-Carmona, V J Traver, J S Sánchez, et al. Online reconstruction-free single-pixel image classification. Image and Vision Computing, 86, 28-37(2019).
[10] He X, Zhao S, Wang L. Ghost hwritten digit recognition based on deep learning [J]. arXiv preprint arXiv, 2020: 2004.02068.
[11] Rizvi S, Cao J, Hao Q. Highspeed imagefree target detection classification in singlepixel imaging [C]SPIE Future Sensing Technologies. International Society f Optics Photonics, 2020, 11525: 115250 X.
[12] H Fu, L Bian, J Zhang. Single-pixel sensing with optimal binarized modulation. Optics Letters, 45, 3111-3114(2020).
[13] X Lin, Y Rivenson, N T Yardimci, et al. All-optical machine learning using diffractive deep neural networks. Science, 361, 1004-1008(2018).
[14] T Zhou, X Lin, J Wu, et al. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nature Photonics, 15, 367-373(2021).
[15] Y Shen, N C Harris, S Skirlo, et al. Deep learning with coherent nanophotonic circuits. Nature Photonics, 11, 441-446(2017).
[16] S Jiao, J Feng, Y Gao, et al. Optical machine learning with incoherent light and a single-pixel detector. Optics Letters, 44, 5186-5189(2019).
[17] Zhiping Zhou, Pengfei Xu, Xiaowen Dong. Computing on silicon photonic platform. Chinese Journal of Lasers, 47, 0600001(2020).
[18] Z Zhang, X Li, S Zheng, et al. Image-free classification of fast-moving objects using “learned” structured illumination and single-pixel detection. Optics Express, 28, 13269-13278(2020).
[19] Y LeCun, L Bottou, Y Bengio, et al. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86, 2278-2324(1998).
[20] LeCun Y, Ctes C, Burges C J C. THE MNIST DATABASE of hwritten digits [EBOL]. [20211116] http:yann.lecun.comexdbmnist.
[21] Z Zhang, X Wang, G Zheng, et al. Fast Fourier single-pixel imaging via binary illumination. Scientific Reports, 7, 1-9(2017).
[22] Xiao H, Rasul K, Vollgraf R. Fashionmnist: A novel image dataset f benchmarking machine learning algithms [J]. arXiv preprint arXiv, 2017: 1708.07747.
[23] Burges C J C, Schlkopf B. Improving the accuracy speed of suppt vect machines [C]Proceedings of the 9 th International Conference on Neural Infmation Processing Systems, 1996: 375381.
[24] Ciresan D C, Meier U, Gambardella L M, et al. Deep big simple neural s excel on hwritten digit recognition [J]. arXiv preprint arXiv, 2010: 1003.0358.
[25] Ciresan D C, Meier U, Mi J, et al. Flexible, high perfmance convolutional neural wks f image classification [C]Twentysecond International Joint Conference on Artificial Intelligence, 2011: 12371242.
