  We develop a novel optical neural network (ONN) framework which introduces a
degree of scalar invariance to image classification estima- tion. Taking a hint
from the human eye, which has higher resolution near the center of the retina,
images are broken out into multiple levels of varying zoom based on a focal
point. Each level is passed through an identical convolutional neural network
(CNN) in a Siamese fashion, and the results are recombined to produce a high
accuracy estimate of the object class. ONNs act as a wrapper around existing
CNNs, and can thus be applied to many existing algorithms to produce notable
accuracy improvements without having to change the underlying architecture.
