Deep neural networks (DNNs) are used to classify and reconstruct the input images from the intensity of the speckle patterns that result after the inputs are propagated through multimode fiber (MMF). We were able to demonstrate this result for fibers up to 1 km long by training the DNNs with a database of 16,000 handwritten digits. Better recognition accuracy was obtained when the DNNs were trained to first reconstruct the input and then classify based on the recovered image. We observed remarkable robustness against environmental instabilities and tolerance to deviations of the input pattern from the patterns with which the DNN was originally trained.

中文翻译：

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学习透视多模光纤

深度神经网络（DNN）用于根据输入通过多模光纤（MMF）传播后产生的斑点图案的强度对输入图像进行分类和重构。通过使用16,000个手写数字的数据库训练DNN，我们能够证明长达1 km的光纤的这一结果。当训练DNN首先重构输入然后基于恢复的图像进行分类时，可以获得更好的识别精度。我们观察到了对环境不稳定性的出色鲁棒性，以及对输入模式与最初训练DNN的模式的偏差的容忍度。