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Optical Memory and Neural Networks
Erschienen in: Optical Memory and Neural Networks 1/2023

01.11.2023

Investigation of the Training Data Set Influence on the Accuracy of the Optical Laguerre-Gaussian Modes Recognition

verfasst von: A. V. Bekhterev

Erschienen in: Optical Memory and Neural Networks | Sonderheft 1/2023

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Abstract

This paper investigates the accuracy of convolutional neural network recognition of Laguerre and Hermite-Gauss optical modes with geometric distortions in the form of affine transformations. The influence of training data sampling on the accuracy is analyzed. The ability of a convolutional neural network to recognize Laguerre and Hermite-Gaussian optical modes with geometric distortions caused by environmental distortions and described by affine transformations was also examined.
Vorheriger Artikel Modulation Instability in Driven VCSELs Above Threshold
Nächster Artikel Measuring Singularities of Vector Structured LG Beams and Stokes Vortices via Intensity Moments Technique

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Metadaten
Titel
Investigation of the Training Data Set Influence on the Accuracy of the Optical Laguerre-Gaussian Modes Recognition
verfasst von
A. V. Bekhterev
Publikationsdatum
01.11.2023
Verlag
Pleiades Publishing
Erschienen in
Optical Memory and Neural Networks / Ausgabe Sonderheft 1/2023
Print ISSN: 1060-992X
Elektronische ISSN: 1934-7898
DOI
https://doi.org/10.3103/S1060992X2305003X

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