One primary point of interest that is beginning to attract attention is the quality of the latent space itself. Because in data-rich regimes, VAEs and other generative models can be extremely powerful, the focus in past work has been primarily on improving performance . However, construction of an interpretable and well-organized latent space not only tends to make targeted feature sample generation easier, it also significantly improves the ability of the user to apply domain expertise to the problem at hand. Therefore, although we do not ignore model performance, a major focus of this article is on interpretability, trustworthiness, and organization of the latent spaces themselves.