Optical Neural Networks

Grant Fennessy; Yevgeniy Vorobeychik

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arXiv · 1805.06082

Optical Neural Networks

Grant Fennessy·Yevgeniy Vorobeychik

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Abstract

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.

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Grant Fennessy, Yevgeniy Vorobeychik. 2018-05-16. Optical Neural Networks. https://arxiv.org/abs/1805.06082

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