Scale-, shift- and rotation-invariant diffractive optical networks
- URL: http://arxiv.org/abs/2010.12747v1
- Date: Sat, 24 Oct 2020 02:18:39 GMT
- Title: Scale-, shift- and rotation-invariant diffractive optical networks
- Authors: Deniz Mengu, Yair Rivenson, Aydogan Ozcan
- Abstract summary: Diffractive Deep Neural Networks (D2NNs) harness light-matter interaction over a series of trainable surfaces to compute a desired statistical inference task.
Here, we demonstrate a new training strategy for diffractive networks that introduces input object translation, rotation and/or scaling during the training phase.
This training strategy successfully guides the evolution of the diffractive optical network design towards a solution that is scale-, shift- and rotation-invariant.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent research efforts in optical computing have gravitated towards
developing optical neural networks that aim to benefit from the processing
speed and parallelism of optics/photonics in machine learning applications.
Among these endeavors, Diffractive Deep Neural Networks (D2NNs) harness
light-matter interaction over a series of trainable surfaces, designed using
deep learning, to compute a desired statistical inference task as the light
waves propagate from the input plane to the output field-of-view. Although,
earlier studies have demonstrated the generalization capability of diffractive
optical networks to unseen data, achieving e.g., >98% image classification
accuracy for handwritten digits, these previous designs are in general
sensitive to the spatial scaling, translation and rotation of the input
objects. Here, we demonstrate a new training strategy for diffractive networks
that introduces input object translation, rotation and/or scaling during the
training phase as uniformly distributed random variables to build resilience in
their blind inference performance against such object transformations. This
training strategy successfully guides the evolution of the diffractive optical
network design towards a solution that is scale-, shift- and
rotation-invariant, which is especially important and useful for dynamic
machine vision applications in e.g., autonomous cars, in-vivo imaging of
biomedical specimen, among others.
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