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All-optical machine learning using diffractive deep neural networks

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Science  07 Sep 2018:
Vol. 361, Issue 6406, pp. 1004-1008
DOI: 10.1126/science.aat8084

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  • A "diffractive deep neural network" is NOT an all-optical implementation of a conventional deep neural network
    • Haiqing Wei, Scientist, Ambow Research Institute
    • Other Contributors:
      • Gang Huang, Scientist, Ambow Research Institute
      • Xiuqing Wei, Professor, The Third Affiliated Hospital of Sun Yat-sen University
      • Yanlong Sun, Professor, Texas A&M University
      • Hongbin Wang, Professor, Texas A&M University

    Here Lin et al. report a remarkable proposal that employs a passive, strictly linear optical setup to perform pattern classifications. But readers are strongly advised to draw a clear distinction between the so-called "diffractive deep neural network" (D2NN) and an all-optical implementation of a deep neural network (DNN) in the canonical sense. While the purported D2NN is devoid of any substantially nonlinear signal processing in the middle (hidden) layers, a conventional DNN incorporates nonlinear activations in its middle (hidden) layers and derives powerful computational advantages from them.

    Interested readers are referred to H. Wei et al., "Comment on 'All-optical machine learning using diffractive deep neural networks'," arXiv:1809.08360 (https://arxiv.org/abs/1809.08360) for detailed discussions, where it has also been rigorously proved that any nonlinearity present or introduced at the output layer of a D2NN or afterward won't be able to boost the pattern discrimination power to beyond the classical Euclidean distance-based algorithms.

    Competing Interests: None declared.

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