  An input-output table is an important data for analyzing the economic
situation of a region. Generally, the input-output table for each region
(regional input-output table) in Japan is not always publicly available, so it
is necessary to estimate the table. In particular, various methods have been
developed for estimating input coefficients, which are an important part of the
input-output table. Currently, non-survey methods are often used to estimate
input coefficients because they require less data and computation, but these
methods have some problems, such as discarding information and requiring
additional data for estimation.
  In this study, the input coefficients are estimated by approximating the
generation process with an artificial neural network (ANN) to mitigate the
problems of the non-survey methods and to estimate the input coefficients with
higher precision. To avoid over-fitting due to the small data used, data
augmentation, called mixup, is introduced to increase the data size by
generating virtual regions through region composition and scaling.
  By comparing the estimates of the input coefficients with those of Japan as a
whole, it is shown that the accuracy of the method of this research is higher
and more stable than that of the conventional non-survey methods. In addition,
the estimated input coefficients for the three cities in Japan are generally
close to the published values for each city.
