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Published in AI journal - MDPI , 2023
This paper describes the transformation of a traditional in silico classification network into an optical fully convolutional neural network with high-resolution feature maps and kernels.
Recommended citation: Ibadulla, R.; Chen, T.M.; Reyes-Aldasoro, C.C. FatNet: High-Resolution Kernels for Classification Using Fully Convolutional Optical Neural Networks. AI 2023, 4, 361-374. https://doi.org/10.3390/ai4020018 https://www.mdpi.com/2673-2688/4/2/18
Published in SPIE Photonics West- AI and Optical Data Sciences V, 2024
This paper describes the advantages and disadvantages of adapting the U-Net architecture from a traditional GPU to a 4f free-space optical environment.
Recommended citation: Riad Ibadulla, Constantino C. Reyes-Aldasoro, and Thomas M. Chen "Fat-U-Net: non-contracting U-Net for free-space optical neural networks", Proc. SPIE 12903, AI and Optical Data Sciences V, 1290308 (13 March 2024); https://doi.org/10.1117/12.3008618 https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12903/1290308/Fat-U-Net--non-contracting-U-Net-for-free/10.1117/12.3008618.short#_=_
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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