Calibration-Free Gradient-Based In-Situ Training for Optical Diffraction Neural Networks

We propose a calibration-free, gradient-based in-situ training method for optical diffraction neural networks. Leveraging wavefront interferometry and spatial rotational invariance to handle misalignment, we maintain 87.31% accuracy even with a ±15° error of optical system.

Authors

Qiaomu Hu (ORCID: https://orcid.org/0000-0001-6375-6161)
Minming Zhang (ORCID: https://orcid.org/0000-0002-3742-1445)
Nilton Li (ORCID: https://orcid.org/0000-0001-9112-5068)
Shuang Zheng
Tiange Wu
Rui Zeng

Institutions

Center of Hubei Cooperative Innovation for Emissions Trading System (CN)
Huazhong University of Science and Technology (CN)

Publication Details

Published: 2025-01-01
DOI: https://doi.org/10.1364/cleo_si.2025.ss165_5
Primary Topic: Neural Networks and Reservoir Computing
Type: article
Field-Weighted Citation Impact: 0.00

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article

Calibration-Free Gradient-Based In-Situ Training for Optical Diffraction Neural Networks

Qiaomu Hu, Minming Zhang, Nilton Li, Shuang Zheng et al.

Jan 1, 2025

Neural Networks and Reservoir Computing

article

Calibration-Free Gradient-Based In-Situ Training for Optical Diffraction Neural Networks

Qiaomu Hu, Minming Zhang, Nilton Li, Shuang Zheng, Tiange Wu, Rui Zeng

January 1, 2025article en

Abstract

We propose a calibration-free, gradient-based in-situ training method for optical diffraction neural networks. Leveraging wavefront interferometry and spatial rotational invariance to handle misalignment, we maintain 87.31% accuracy even with a ±15° error of optical system.

Center of Hubei Cooperative Innovation for Emissions Trading System (CN), Huazhong University of Science and Technology (CN)

Openalex Percentile: Top 86%

Neural Networks and Reservoir Computing

DOI Source OpenAlex

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