Ensemble learning of diffractive optical networks

• Main Authors: Md Sadman Sakib Rahman, Jingxi Li, Deniz Mengu, Yair Rivenson, Aydogan Ozcan
• Format: Article
• Language: English
• Published: Nature Publishing Group 2021-01-01
• Series: Light: Science & Applications
• Online Access: https://doi.org/10.1038/s41377-020-00446-w

Diffractive networks light the way for better optical image classification Scientists in USA have demonstrated significant improvements in the performance of diffractive optical networks, marking a major step forward for their use in optics-based computation and machine learning. There is renewed interest in optical computing hardware due to its potential advantages, including parallelization, power efficiency, and computation speed. Diffractive optical networks utilize deep learning-based design of successive diffractive layers to all-optically process information as the light is transmitted from the input to the output plane. Led by Aydogan Ozcan, a team of researchers from University of California, Los Angeles has significantly improved the statistical inference performance of diffractive optical networks using feature engineering and ensemble learning. Using a pruning algorithm, they searched through 1,252 unique diffractive networks to design ensembles of desired size that substantially improve the overall system’s all-optical image classification accuracy.