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

Kiana Farhadyar, Federico Bonofiglio, Daniela Zoeller +1

Synthetic data generation is of great interest in diverse applications, such as for privacy protection. Deep generative models, such as variational autoencoders (VAEs), are a popul…