This may be due to the fact that the latent variable priors of the ml-VAE-D model are inferred from the data, rather than a fixed standard Gaussian distribution.
the model is endowed with more flexibility to encode informative semantic features in the latent variables, yet matching their posterior distributions to the corresponding priors.