Scale-, Shift-, and Rotation-Invariant Diffractive Optical Networks

Recent research efforts in optical computing have gravitated toward developing optical neural networks that aim to benefit from the processing speed and parallelism of optics/photonics in machine learning applications. Among these endeavors, Diffractive Deep Neural Networks (D2NNs) harness light-matter interaction over a series of trainable surfaces, designed using deep learning, to compute a desired statistical inference task as the light waves propagate from the input plane to the output field-of-view. Although earlier studies have demonstrated the generalization capability of diffractive optical networks to unseen data, achieving, e.g., >98% image classification accuracy for handwritten digits, these previous designs are in general sensitive to the spatial scaling, translation, and rotation of the input objects. Here, we demonstrate a new training strategy for diffractive networks that introduces input object translation, rotation, and/or scaling during the training phase as uniformly distributed random variables to build resilience in their blind inference performance against such object transformations. This training strategy successfully guides the evolution of the diffractive optical network design toward a solution that is scale-, shift-, and rotation-invariant, which is especially important and useful for dynamic machine vision applications in, e.g., autonomous cars, in vivo imaging of biomedical specimen, among others.