ONNet is an open-source Python/C++ package for the optical neural networks, which provides many tools for researchers studying optical neural networks. Some new models are as follows:
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Express Wavenet uses random shift wavelet pattern to modulate the phase of optical waves, which only need one percent of the parameters and the accuracy is still high. In the MNIST dataset, it only needs 1229 parameters to get accuracy of 92%, while DDNet needs 125440 parameters. .[2]
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Each layer have multiple frequency-channels (optical distributions at different frequency). These channels are merged at the output plane with weighting coefficient. [1]
Optical neural network(ONN) is a novel machine learning framework on the physical principles of optics, which is still in its infancy and shows great potential. ONN tries to find optimal modulation parameters to change the phase, amplitude or other physical variable of optical wave propagation. So in the final output plane, the optical distribution has special pattern which is the indicator of object’s class or value. ONN opens new doors for the machine learning.
I used to think that "ONN opens new doors for the machine learning", but now it seems only few people admit the significance of ONN to machine learning. It's really hard to explain why ONN performs so poorly on widely used data sets(CIFAR...), let alone Imagenet!
Fortunately, I find the optical diffraction model has subtle connection with some mathematical models, which is worthy of further study.
---2/27/2022
Please use the following bibtex entry:
[1] Xinyu, Zhang, Jiashuo Shi, and Yingshi Chen. "A Broad-Spectrum Diffractive Network via Ensemble Learning." Opt. Lett 46 (2021): 14.
[2] Chen, Yingshi, et al."An optical diffractive deep neural network with multiple frequency-channels." arXiv preprint arXiv:1912.10730 (2019).
[3] Chen, Yingshi, et al. "Express Wavenet: A lower parameter optical neural network with random shift wavelet pattern." Optics Communications 485 (2021): 126709.
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More testing datasets
Cifar, ImageNet ......
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More models
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More papers.
The provided implementation is strictly for academic purposes only. If anyone is interested in using our technology for any commercial use, please contact us.
Yingshi Chen (gsp.cys@gmail.com)
QQ group: 1001583663