Ensemble learning of diffractive optical networks

Md Sadman Sakib Rahman; Jingxi Li; Deniz Mengu; Yair Rivenson; Aydogan Ozcan

doi:10.1038/s41377-020-00446-w

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Ensemble learning of diffractive optical networks

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- Ensemble learning of diffractive optical networks
- Ensemble learning of diffractive optical networks
- LSA 2021年10卷第1期页码：123-135
- Affiliations：
  
  1.Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA
  2.Bioengineering Department, University of California, Los Angeles, CA 90095, USA
  3.California NanoSystems Institute (CNSI), University of California, Los Angeles, CA 90095, USA
- Author bio：
  
  Aydogan Ozcan (ozcan@ucla.edu)
- Funds：
- DOI：10.1038/s41377-020-00446-w
  中图分类号：
- 纸质出版日期：2021，
  
  网络出版日期：2021-01-11，
  
  收稿日期：2020-09-09，
  
  修回日期：2020-11-27，
  
  录用日期：2020-11-30
- Accepted：
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Ensemble learning of diffractive optical networks[J]. LSA, 2021,10(1):123-135.

Rahman, M. S. S. et al. Ensemble learning of diffractive optical networks. Light: Science & Applications, 10, 123-135 (2021).
Ensemble learning of diffractive optical networks[J]. LSA, 2021,10(1):123-135. DOI： 10.1038/s41377-020-00446-w.

Rahman, M. S. S. et al. Ensemble learning of diffractive optical networks. Light: Science & Applications, 10, 123-135 (2021). DOI： 10.1038/s41377-020-00446-w.

摘要

Abstract

A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine learning. Specifically

there has been a revival of interest in optical computing hardware due to its potential advantages for machine learning tasks in terms of parallelization

power efficiency and computation speed. Diffractive deep neural networks (D

NNs) form such an optical computing framework that benefits from deep learning-based design of successive diffractive layers to all-optically process information as the input light diffracts through these passive layers. D

NNs have demonstrated success in vari

ous tasks

including object classification

the spectral encoding of information

optical pulse shaping and imaging. Here

we substantially improve the inference performance of diffractive optical networks using feature engineering and ensemble learning. After independently training 1252 D

NNs that were diversely engineered with a variety of passive input filters

we applied a pruning algorithm to select an optimized ensemble of D

NNs that collectively improved the image classification accuracy. Through this pruning

we numerically demonstrated that ensembles of

= 14 and

= 30 D

NNs achieve blind testing accuracies of 61.14 ± 0.23% and 62.13 ± 0.05%

respectively

on the classification of CIFAR-10 test images

providing an inference improvement of

16% compared to the average performance of the individual D

NNs within each ensemble. These results constitute the highest inference accuracies achieved to date by any diffractive optical neural network design on the same dataset and might provide a significant leap to extend the application space of diffractive optical image classification and machine vision systems.

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