  For many analytical problems the challenge is to handle huge amounts of
available data. However, there are data science application areas where
collecting information is difficult and costly, e.g., in the study of
geological phenomena, rare diseases, faults in complex systems, insurance
frauds, etc. In many such cases, generators of synthetic data with the same
statistical and predictive properties as the actual data allow efficient
simulations and development of tools and applications. In this work, we propose
the incorporation of Monte Carlo Dropout method within Autoencoder (MCD-AE) and
Variational Autoencoder (MCD-VAE) as efficient generators of synthetic data
sets. As the Variational Autoencoder (VAE) is one of the most popular generator
techniques, we explore its similarities and differences to the proposed
methods. We compare the generated data sets with the original data based on
statistical properties, structural similarity, and predictive similarity. The
results obtained show a strong similarity between the results of VAE, MCD-VAE
and MCD-AE; however, the proposed methods are faster and can generate values
similar to specific selected initial instances.
