Improving the Inference Accuracy of Diffractive Optical Neural Networks Using Class-specific Differential Detection

Jingxi Li; Jingxi Li; Jingxi Li; Deniz Mengu; Deniz Mengu; Deniz Mengu; Yi Luo; Yi Luo; Yi Luo; Yair Rivenson; Yair Rivenson; Yair Rivenson; Aydogan Ozcan; Aydogan Ozcan; Aydogan Ozcan

doi:10.1364/CLEO_SI.2020.SM1E.3

Conference on Lasers and Electro-Optics
OSA Technical Digest (Optica Publishing Group, 2020),
paper SM1E.3
•https://doi.org/10.1364/CLEO_SI.2020.SM1E.3

Improving the Inference Accuracy of Diffractive Optical Neural Networks Using Class-specific Differential Detection

Jingxi Li, Deniz Mengu, Yi Luo, Yair Rivenson, and Aydogan Ozcan

CLEO: Science and Innovations 2020

Washington, DC United States
10–15 May 2020
ISBN: 978-1-943580-76-7

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Integrated Photonics for Neural Networks and Deep Learning (SM1E)

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J. Li, D. Mengu, Y. Luo, Y. Rivenson, and A. Ozcan, "Improving the Inference Accuracy of Diffractive Optical Neural Networks Using Class-specific Differential Detection," in Conference on Lasers and Electro-Optics, OSA Technical Digest (Optica Publishing Group, 2020), paper SM1E.3.

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Abstract

We report all-optical object classification systems that are based on class-specific design of diffractive neural networks followed by a differential detection scheme. The blind inference accuracies achieved through this framework are significantly enhanced.

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