Ensemble learning of diffractive optical networks

by   Md Sadman Sakib Rahman, et al.

A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine learning. Specifically, there has been a revival of interest in optical computing hardware, due to its potential advantages for machine learning tasks in terms of parallelization, power efficiency and computation speed. Diffractive Deep Neural Networks (D2NNs) form such an optical computing framework, which benefits from deep learning-based design of successive diffractive layers to all-optically process information as the input light diffracts through these passive layers. D2NNs have demonstrated success in various tasks, including e.g., object classification, spectral-encoding of information, optical pulse shaping and imaging, among others. Here, we significantly improve the inference performance of diffractive optical networks using feature engineering and ensemble learning. After independently training a total of 1252 D2NNs that were diversely engineered with a variety of passive input filters, we applied a pruning algorithm to select an optimized ensemble of D2NNs that collectively improve their image classification accuracy. Through this pruning, we numerically demonstrated that ensembles of N=14 and N=30 D2NNs achieve blind testing accuracies of 61.14 CIFAR-10 test images, providing an inference improvement of >16 the average performance of the individual D2NNs within each ensemble. These results constitute the highest inference accuracies achieved to date by any diffractive optical neural network design on the same dataset and might provide a significant leapfrog to extend the application space of diffractive optical image classification and machine vision systems.


page 17

page 19

page 21

page 22


Time-lapse image classification using a diffractive neural network

Diffractive deep neural networks (D2NNs) define an all-optical computing...

Class-specific Differential Detection in Diffractive Optical Neural Networks Improves Inference Accuracy

Diffractive deep neural networks have been introduced earlier as an opti...

Analysis of Diffractive Optical Neural Networks and Their Integration with Electronic Neural Networks

Optical machine learning offers advantages in terms of power efficiency,...

Terahertz Pulse Shaping Using Diffractive Legos

Recent advances in deep learning have been providing non-intuitive solut...

Fast and Accurate Inference with Adaptive Ensemble Prediction in Image Classification with Deep Neural Networks

Ensembling multiple predictions is a widely used technique to improve th...

Evaluation of Confidence-based Ensembling in Deep Learning Image Classification

Ensembling is a successful technique to improve the performance of machi...

Cascadable all-optical NAND gates using diffractive networks

Owing to its potential advantages such as scalability, low latency and p...

Please sign up or login with your details

Forgot password? Click here to reset