• Photonics Research
  • Vol. 12, Issue 6, 1159 (2024)
Minjia Zheng1, Wenzhe Liu2,5,*, Lei Shi1,2,3,4,6,*, and Jian Zi1,2,3,4,7,*
Author Affiliations
  • 1State Key Laboratory of Surface Physics, Key Laboratory of Micro- and Nano-Photonic Structures (Ministry of Education) and Department of Physics, Fudan University, Shanghai 200433, China
  • 2Institute for Nanoelectronic Devices and Quantum Computing, Fudan University, Shanghai 200433, China
  • 3Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
  • 4Shanghai Research Center for Quantum Sciences, Shanghai 210315, China
  • 5e-mail: wliubh@connect.ust.hk
  • 6e-mail: lshi@fudan.edu.cn
  • 7e-mail: jzi@fudan.edu.cn
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    DOI: 10.1364/PRJ.513845 Cite this Article Set citation alerts
    Minjia Zheng, Wenzhe Liu, Lei Shi, Jian Zi, "Diffractive neural networks with improved expressive power for gray-scale image classification," Photonics Res. 12, 1159 (2024) Copy Citation Text show less
    References

    [1] Y. LeCun, Y. Bengio, G. Hinton. Deep learning. Nature, 521, 436-444(2015).

    [2] R. Szeliski. Computer Vision: Algorithms and Applications(2010).

    [3] A. Krizhevsky, F. Pereira, C. Burges, I. Sutskever, L. Bottou, G. E. Hinton, K. Weinberger. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25(2012).

    [4] Y. LeCun, D. Touretzky, B. Boser, J. Denker. Handwritten digit recognition with a back-propagation network. Advances in Neural Information Processing Systems, 2(1989).

    [5] R. M. Haralick, L. G. Shapiro. Image segmentation techniques. Comput. Vis. Graph. Image Process., 29, 100-132(1985).

    [6] S. Minaee, Y. Y. Boykov, F. Porikli. Image segmentation using deep learning: a survey. IEEE Trans. Pattern Anal. Mach. Intell., 44, 3523-3542(2021).

    [7] A. Borji, M.-M. Cheng, H. Jiang. Salient object detection: a benchmark. IEEE Trans. Image Process., 24, 5706-5722(2015).

    [8] H. Fu, X. Cao, Z. Tu. Cluster-based co-saliency detection. IEEE Trans. Image Process., 22, 3766-3778(2013).

    [9] W. Wang, J. Shen, L. Shao. Video salient object detection via fully convolutional networks. IEEE Trans. Image Process., 27, 38-49(2017).

    [10] A. Wang, M. Wang. RGB-D salient object detection via minimum barrier distance transform and saliency fusion. IEEE Signal Process. Lett., 24, 663-667(2017).

    [11] A. Chaurasia, E. Culurciello. Linknet: exploiting encoder representations for efficient semantic segmentation. IEEE Visual Communications and Image Processing (VCIP), 1-4(2017).

    [12] O. Russakovsky, J. Deng, H. Su. Imagenet large scale visual recognition challenge. Int. J. Comput. Vis., 115, 211-252(2015).

    [13] W. Zhang, K. Itoh, J. Tanida. Parallel distributed processing model with local space-invariant interconnections and its optical architecture. Appl. Opt., 29, 4790-4797(1990).

    [14] D. Powell, M. Duffy. Neural networks and statistical models. Proceedings of the Nineteenth Annual SAS Users Group International Conference, 806-814(1994).

    [15] R. Hamerly, L. Bernstein, A. Sludds. Large-scale optical neural networks based on photoelectric multiplication. Phys. Rev. X, 9, 021032(2019).

    [16] J. Bueno, S. Maktoobi, L. Froehly. Reinforcement learning in a large-scale photonic recurrent neural network. Optica, 5, 756-760(2018).

    [17] T. W. Hughes, M. Minkov, Y. Shi. Training of photonic neural networks through in situ backpropagation and gradient measurement. Optica, 5, 864-871(2018).

    [18] P. R. Prucnal, B. J. Shastri, M. C. Teich. Neuromorphic Photonics(2017).

    [19] D. Pérez, I. Gasulla, P. D. Mahapatra. Principles, fundamentals, and applications of programmable integrated photonics. Adv. Opt. Photon., 12, 709-786(2020).

    [20] X. Xu, M. Tan, B. Corcoran. 11 tops photonic convolutional accelerator for optical neural networks. Nature, 589, 44-51(2021).

    [21] B. J. Shastri, A. N. Tait, T. Ferreira de Lima. Photonics for artificial intelligence and neuromorphic computing. Nat. Photonics, 15, 102-114(2021).

    [22] J. Feldmann, N. Youngblood, M. Karpov. Parallel convolutional processing using an integrated photonic tensor core. Nature, 589, 52-58(2021).

    [23] X. Lin, Y. Rivenson, N. T. Yardimci. All-optical machine learning using diffractive deep neural networks. Science, 361, 1004-1008(2018).

    [24] Y. Shen, N. C. Harris, S. Skirlo. Deep learning with coherent nanophotonic circuits. Nat. Photonics, 11, 441-446(2017).

    [25] A. N. Tait, T. F. De Lima, E. Zhou. Neuromorphic photonic networks using silicon photonic weight banks. Sci. Rep., 7, 7430(2017).

    [26] M. Hermans, M. Burm, T. Van Vaerenbergh. Trainable hardware for dynamical computing using error backpropagation through physical media. Nat. Commun., 6, 6729(2015).

    [27] D. Brunner, M. C. Soriano, C. R. Mirasso. Parallel photonic information processing at gigabyte per second data rates using transient states. Nat. Commun., 4, 1364(2013).

    [28] M. M. P. Fard, I. A. D. Williamson, M. Edwards. Experimental realization of arbitrary activation functions for optical neural networks. Opt. Express, 28, 12138-12148(2020).

    [29] S. Pai, Z. Sun, T. W. Hughes. Experimentally realized in situ backpropagation for deep learning in photonic neural networks. Science, 380, 398-404(2023).

    [30] G. Wetzstein, A. Ozcan, S. Gigan. Inference in artificial intelligence with deep optics and photonics. Nature, 588, 39-47(2020).

    [31] I. Chakraborty, G. Saha, A. Sengupta. Toward fast neural computing using all-photonic phase change spiking neurons. Sci. Rep., 8, 12980(2018).

    [32] J. Chang, V. Sitzmann, X. Dun. Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification. Sci. Rep., 8, 12324(2018).

    [33] L. Mennel, J. Symonowicz, S. Wachter. Ultrafast machine vision with 2D material neural network image sensors. Nature, 579, 62-66(2020).

    [34] Y. Zuo, B. Li, Y. Zhao. All-optical neural network with nonlinear activation functions. Optica, 6, 1132-1137(2019).

    [35] X. Luo, Y. Hu, X. Ou. Metasurface-enabled on-chip multiplexed diffractive neural networks in the visible. Light Sci. Appl., 11, 158(2022).

    [36] F. Ashtiani, A. J. Geers, F. Aflatouni. An on-chip photonic deep neural network for image classification. Nature, 606, 501-506(2022).

    [37] T. W. Hughes, I. A. Williamson, M. Minkov. Wave physics as an analog recurrent neural network. Sci. Adv., 5, eaay6946(2019).

    [38] H. Dou, Y. Deng, T. Yan. Residual D2NN: training diffractive deep neural networks via learnable light shortcuts. Opt. Lett., 45, 2688-2691(2020).

    [39] J. Li, Y.-C. Hung, O. Kulce. Polarization multiplexed diffractive computing: all-optical implementation of a group of linear transformations through a polarization-encoded diffractive network. Light Sci. Appl., 11, 153(2022).

    [40] M. S. S. Rahman, X. Yang, J. Li. Universal linear intensity transformations using spatially-incoherent diffractive processors. arXiv(2023).

    [41] B. Bai, Y. Li, Y. Luo. All-optical image classification through unknown random diffusers using a single-pixel diffractive network. Light Sci. Appl., 12, 69(2023).

    [42] C. Qian, X. Lin, X. Lin. Performing optical logic operations by a diffractive neural network. Light Sci. Appl., 9, 59(2020).

    [43] S. Jiao, J. Feng, Y. Gao. Optical machine learning with incoherent light and a single-pixel detector. Opt. Lett., 44, 5186-5189(2019).

    [44] Z. Wu, M. Zhou, E. Khoram. Neuromorphic metasurface. Photon. Res., 8, 46-50(2020).

    [45] Z. Wu, Z. Yu. Small object recognition with trainable lens. APL Photon., 6, 071301(2021).

    [46] T. Zhou, X. Lin, J. Wu. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nat. Photonics, 15, 367-373(2021).

    [47] H. Chen, J. Feng, M. Jiang. Diffractive deep neural networks at visible wavelengths. Engineering, 7, 1483-1491(2021).

    [48] Y. Hu, X. Luo, Y. Chen. 3D-integrated metasurfaces for full-colour holography. Light Sci. Appl., 8, 86(2019).

    [49] Y. Chen, Z. Shu, S. Zhang. Sub-10 nm fabrication: methods and applications. Int. J. Extreme Manuf., 3, 032002(2021).

    [50] M. Zheng, L. Shi, J. Zi. Optimize performance of a diffractive neural network by controlling the Fresnel number. Photon. Res., 10, 2667-2676(2022).

    [51] H. Xiao, K. Rasul, R. Vollgraf. Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv(2017).

    [52] Y. LeCun, L. Bottou, Y. Bengio. Gradient-based learning applied to document recognition. Proc. IEEE, 86, 2278-2324(1998).

    [53] J. Li, D. Mengu, Y. Luo. Class-specific differential detection in diffractive optical neural networks improves inference accuracy. Adv. Photon., 1, 046001(2019).

    [54] D. Mengu, Y. Luo, Y. Rivenson. Analysis of diffractive optical neural networks and their integration with electronic neural networks. IEEE J. Sel. Top. Quantum Electron., 26, 3700114(2019).

    Minjia Zheng, Wenzhe Liu, Lei Shi, Jian Zi, "Diffractive neural networks with improved expressive power for gray-scale image classification," Photonics Res. 12, 1159 (2024)
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