Generating Data using Monte Carlo Dropout
This addresses data scarcity issues in fields like geology and rare disease study, though it is incremental as it builds on existing VAE techniques.
The authors tackled the problem of generating synthetic data for domains where data collection is difficult by incorporating Monte Carlo Dropout into Autoencoders and Variational Autoencoders, resulting in methods that are faster than VAE and can generate values similar to specific instances.
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.