Multi-mode fiber reservoir computing overcomes shallow neural networks
classifiers
- URL: http://arxiv.org/abs/2210.04745v2
- Date: Fri, 26 May 2023 14:40:26 GMT
- Title: Multi-mode fiber reservoir computing overcomes shallow neural networks
classifiers
- Authors: Daniele Ancora, Matteo Negri, Antonio Gianfrate, Dimitris
Trypogeorgos, Lorenzo Dominici, Daniele Sanvitto, Federico Ricci-Tersenghi,
Luca Leuzzi
- Abstract summary: We recast multi-mode optical fibers into random hardware projectors, transforming an input dataset into a speckled image set.
We find that the hardware operates in a flatter region of the loss landscape when trained on fiber data, which aligns with the current theory of deep neural networks.
- Score: 8.891157811906407
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In the field of disordered photonics, a common objective is to characterize
optically opaque materials for controlling light delivery or performing
imaging. Among various complex devices, multi-mode optical fibers stand out as
cost-effective and easy-to-handle tools, making them attractive for several
tasks. In this context, we leverage the reservoir computing paradigm to recast
these fibers into random hardware projectors, transforming an input dataset
into a higher dimensional speckled image set. The goal of our study is to
demonstrate that using such randomized data for classification by training a
single logistic regression layer improves accuracy compared to training on
direct raw images. Interestingly, we found that the classification accuracy
achieved using the reservoir is also higher than that obtained with the
standard transmission matrix model, a widely accepted tool for describing light
transmission through disordered devices. We find that the reason for such
improved performance could be due to the fact that the hardware classifier
operates in a flatter region of the loss landscape when trained on fiber data,
which aligns with the current theory of deep neural networks. These findings
strongly suggest that multi-mode fibers possess robust generalization
properties, positioning them as promising tools for optically-assisted neural
networks. With this study, in fact, we want to contribute to advancing the
knowledge and practical utilization of these versatile instruments, which may
play a significant role in shaping the future of machine learning.
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