Comment on All-optical machine learning using diffractive deep neural networks

Lin et al. (Reports, 7 September 2018, p. 1004) reported a remarkable proposal that employs a passive, strictly linear optical setup to perform pattern classifications. But interpreting the multilayer diffractive setup as a deep neural network and advocating it as an all-optical deep learning framework are not well justified and represent a mischaracterization of the system by overlooking its defining characteristics of perfect linearity and strict passivity.



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All-Optical Machine Learning Using Diffractive Deep Neural Networks

We introduce an all-optical Diffractive Deep Neural Network (D2NN) archi...

Response to Comment on "All-optical machine learning using diffractive deep neural networks"

In their Comment, Wei et al. (arXiv:1809.08360v1 [cs.LG]) claim that our...

An optical diffractive deep neural network with multiple frequency-channels

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Optical machine learning with incoherent light and a single-pixel detector

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Deep Neural Networks for Computational Optical Form Measurements

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Class-specific Differential Detection in Diffractive Optical Neural Networks Improves Inference Accuracy

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Disclosure statement

The authors have no potential financial or non-financial conflicts of interest.

Notes on contributors

All authors contributed equally in researching, collating, and writing.


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