Generating Data using Monte Carlo Dropout

TL;DR

This paper introduces Monte Carlo Dropout-enhanced autoencoders as efficient methods for generating synthetic data that closely resembles real data in statistical and predictive properties, especially useful when data collection is costly.

Contribution

It proposes novel MCD-AE and MCD-VAE models that improve synthetic data generation speed and specificity compared to traditional VAEs.

Findings

MCD-AE and MCD-VAE produce data similar to original datasets

The proposed methods are faster than standard VAEs

Generated data maintains statistical and predictive properties

Abstract

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.