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9 March 2022 Diffractive optical networks with scale, shift, and rotation invariance
Deniz Mengu, Yair Rivenson, Aydogan Ozcan
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Proceedings Volume PC12019, AI and Optical Data Sciences III; PC120190M (2022) https://doi.org/10.1117/12.2609500
Event: SPIE OPTO, 2022, San Francisco, California, United States
Abstract
This study reports a new diffractive optical network design strategy that incorporates object scaling, translation, and rotation as part of the forward training model using uniformly-distributed random variables to provide immunity and resilience against such variations at the input object plane. By guiding the evolution of the diffractive layers towards a scale-, shift- and rotation-invariant network solution, this training strategy provides >30-70% improvement in the all-optical blind inference accuracies achieved under various unknown object transformations. This training method constitutes a promising approach to bring the advantages of all-optical diffractive inference with low-latency, power-efficiency, and parallelization to various machine vision applications.
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Deniz Mengu, Yair Rivenson, and Aydogan Ozcan "Diffractive optical networks with scale, shift, and rotation invariance", Proc. SPIE PC12019, AI and Optical Data Sciences III, PC120190M (9 March 2022); https://doi.org/10.1117/12.2609500
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KEYWORDS
Optical networks
Biomedical optics
CARS tomography
Light-matter interactions
Machine learning
Machine vision
Neural networks
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