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In new research, scientists from the lab of professor Aydogan Ozcan at UCLA have demonstrated distinct improvements to the inference and generalization performance of diffractive optical neural networks.
The researchers demonstrated a differential detection scheme where each class is assigned to a separate pair of photodetectors, behind a diffractive optical network. The class inference is made by maximizing the normalized signal difference between the photodetector pairs.
Using this scheme, which involved 10 photodetector pairs behind five diffractive layers with a total of 0.2 million neurons, the researchers achieved blind testing accuracies of 98.54%, 90.54%, and 48.51% for MNIST, Fashion-MNIST, and grayscale CIFAR-10 data…READ MORE