Ensemble learning of diffractive optical networks.

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 (D²NNs) 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. D²NNs have demonstrated success in various tasks, including object classification, the spectral encoding of information, optical pulse shaping and imaging. Here, we subs

SUBMITTER: Rahman MSS 

PROVIDER: S-EPMC7801728 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature