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

TL;DR

This paper defends the original interpretation of Diffractive Deep Neural Networks (D2NN) against claims of mischaracterization, emphasizing the system's nonlinearities, reconfigurability, and depth advantages for improved optical classification performance.

Contribution

The authors clarify that D2NNs incorporate optical nonlinearities and reconfigurability, and demonstrate the depth advantage of multiple diffractive layers for enhanced classification accuracy.

Findings

Multiple diffractive layers improve classification accuracy.

Depth of D2NN enhances signal contrast and diffraction efficiency.

Optical nonlinearities are integral to D2NN performance.

Abstract

In their Comment, Wei et al. (arXiv:1809.08360v1 [cs.LG]) claim that our original interpretation of Diffractive Deep Neural Networks (D2NN) represent a mischaracterization of the system due to linearity and passivity. In this Response, we detail how this mischaracterization claim is unwarranted and oblivious to several sections detailed in our original manuscript (Science, DOI: 10.1126/science.aat8084) that specifically introduced and discussed optical nonlinearities and reconfigurability of D2NNs, as part of our proposed framework to enhance its performance. To further refute the mischaracterization claim of Wei et al., we, once again, demonstrate the depth feature of optical D2NNs by showing that multiple diffractive layers operating collectively within a D2NN present additional degrees-of-freedom compared to a single diffractive layer to achieve better classification accuracy, as well as improved output signal contrast and diffraction efficiency as the number of diffractive layers increase, showing the deepness of a D2NN, and its inherent depth advantage for improved performance. In summary, the Comment by Wei et al. does not provide an amendment to the original teachings of our original manuscript, and all of our results, core conclusions and methodology of research reported in Science (DOI: 10.1126/science.aat8084) remain entirely valid.