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Ensemble learning of diffractive optical networks

Authors :
Md Sadman Sakib Rahman
Jingxi Li
Deniz Mengu
Yair Rivenson
Aydogan Ozcan
Source :
Light: Science & Applications, Vol 10, Iss 1, Pp 1-13 (2021)
Publication Year :
2021
Publisher :
Nature Publishing Group, 2021.

Abstract

Diffractive networks light the way for better optical image classification Scientists in USA have demonstrated significant improvements in the performance of diffractive optical networks, marking a major step forward for their use in optics-based computation and machine learning. There is renewed interest in optical computing hardware due to its potential advantages, including parallelization, power efficiency, and computation speed. Diffractive optical networks utilize deep learning-based design of successive diffractive layers to all-optically process information as the light is transmitted from the input to the output plane. Led by Aydogan Ozcan, a team of researchers from University of California, Los Angeles has significantly improved the statistical inference performance of diffractive optical networks using feature engineering and ensemble learning. Using a pruning algorithm, they searched through 1,252 unique diffractive networks to design ensembles of desired size that substantially improve the overall system’s all-optical image classification accuracy.

Details

Language :
English
ISSN :
20477538
Volume :
10
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Light: Science & Applications
Publication Type :
Academic Journal
Accession number :
edsdoj.1e7f8e78834e4ac0a4524bf8afc1eb97
Document Type :
article
Full Text :
https://doi.org/10.1038/s41377-020-00446-w