Synthetic spectra generated by boundary equilibrium generative adversarial networks and their applications with consensus algorithms.

Abstract

One of the major restrictions in spectroscopic analysis is the limited number of calibrations, especially for biological samples. Meanwhile, there is a lack of effective algorithms to simulate synthetic spectra from the real spectra of limited samples. Thus in this work, a boundary equilibrium generative adversarial network (BEGAN) was proposed to automatically generate synthetic spectra and successfully produce spectra from two datasets. Then, the impact of the diversity ratio was estimated in the aspect of the quality and diversity of the generated spectra by BEGAN, and a negative correlation was found between quality and diversity. Finally, these synthetic spectra are applied in a consensus algorithm named creating diversity partial least squares (CDPLS) to replenish virtual samples in every iteration. Results show that the synthetic spectra generated by BEGAN are of high quality and improve the predictive performance of CDPLS. It can concluded that BEGAN has the potential to generate derived homologous spectra and expand the number of spectra in some small sample sets.