Ensemble learning boosts the inference accuracy of diffractive neural networks

Abstract

We improve the inference performance of diffractive deep neural networks (D2NN) for image classification by utilizing ensemble learning and feature engineering. Through a novel pruning algorithm, we designed an ensemble of e.g., N=14 D2NNs that collectively achieve a blind testing accuracy of 61.14% on the classification of CIFAR-10 images, which provides an improvement of >16% compared to the average performance of the individual D2NNs within the ensemble. These results constitute the highest inference accuracies achieved to date by any diffractive network design and would be broadly useful to create diffractive optical machine learning systems for various imaging and sensing needs.