Class-specific differential detection in diffractive optical neural networks (Conference Presentation)

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

doi:10.1117/12.2544014

9 March 2020 Class-specific differential detection in diffractive optical neural networks (Conference Presentation)

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

Proceedings Volume 11299, AI and Optical Data Sciences; 112990R (2020) https://doi.org/10.1117/12.2544014
Event: SPIE OPTO, 2020, San Francisco, California, United States

Abstract

We introduce a differential measurement scheme in diffractive neural networks, where object-classes are assigned to separate opto-electronic detector pairs and the class-inference is made based on the maximum normalized differential signal. This scheme enables diffractive networks to achieve blind-testing accuracies of 98.54% and 48.51% for MNIST and CIFAR-10 datasets, respectively. These accuracies improve to 98.52% and 50.82%, when differential detection is combined with the joint-training of parallel diffractive networks, with each specializing on a separate object-class. Finally, we report independently-trained diffractive networks that project their output-light onto a common plane to achieve 98.59% and 51.44%, for the same datasets, respectively.

Conference Presentation

Citation Download Citation

Jingxi Li, Deniz Mengu, Yi Luo, Yair Rivenson, and Aydogan Ozcan "Class-specific differential detection in diffractive optical neural networks (Conference Presentation)", Proc. SPIE 11299, AI and Optical Data Sciences, 112990R (9 March 2020); https://doi.org/10.1117/12.2544014

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