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Synthetic data generation is of great interest in diverse applications, suchas for privacy protection. Deep generative models, such as variationalautoencoders (VAEs), are a popular approach for creating such syntheticdatasets from original data. Despite the success of VAEs, there are limitationswhen it comes to the bimodal and skewed marginal distributions. These deviatefrom the unimodal symmetric distributions that are encouraged by the normalityassumption typically used for the latent representations in VAEs. While thereare extensions that assume other distributions for the latent space, this doesnot generally increase flexibility for data with many different distributions.Therefore, we propose a novel method, pre-transformation variationalautoencoders (PTVAEs), to specifically address bimodal and skewed data, byemploying pre-transformations at the level of original variables. Two types oftransformations are used to bring the data close to a normal distribution by aseparate parameter optimization for each variable in a dataset. We compare theperformance of our method with other state-of-the-art methods for syntheticdata generation. In addition to the visual comparison, we use a utilitymeasurement for a quantitative evaluation. The results show that the PTVAEapproach can outperform others in both bimodal and skewed data generation.Furthermore, the simplicity of the approach makes it usable in combination withother extensions of VAE.

Adapting deep generative approaches for getting synthetic data with realistic marginal distributions