Optical neural network could lead to intelligent cameras

Science Daily  August 27, 2019 Researchers at UCLA have made systematic improvements to their earlier work on diffractive optical neural networks, based on a differential measurement technique that mitigates the strict nonnegativity constraint of light intensity. In this differential detection scheme, each class is assigned to a separate pair of detectors, behind a diffractive optical network, and the class inference is made by maximizing the normalized signal difference between the photodetector pairs. Using this differential detection scheme, they numerically achieved blind testing accuracies of 98.54%, 90.54%, and 48.51% for MNIST, Fashion-MNIST, and grayscale CIFAR-10 datasets, respectively. They reduced the crosstalk […]