PoS - Proceedings of Science
Volume 410 - The 5th International Workshop on Deep Learning in Computational Physics (DLCP2021) - Short papers
Use of Conditional Generative Variational Autoencoder Networks to Improve Representativity of Data in Optical Spectroscopy
A. Efitorov*, T. Dolenko, K. Laptinskiy, S. Burikov and S. Dolenko
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Pre-published on: December 10, 2021
Published on: January 12, 2022
In this study, the solution of the inverse problem of spectroscopy of water-ethanol liquid solutions by neural network models is considered. The process of training a neural network requires a large number of patterns, which cannot be obtained by laboratory measurements. In this paper, we demonstrate the possibility of generating an additional array of patterns using a conditional variational autoencoder. The generated patterns have a form similar to real spectra, and they are used to train the neural network for classification, along with the original patterns. As a result of applying this approach, it was possible to improve the quality of solving the inverse problem on real patterns that were not used in the training process
DOI: https://doi.org/10.22323/1.410.0013
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