HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals


Chao Ma, Sebastian Tschiatschek, Yingzhen Li, Richard Turner, Jose Miguel Hernandez-Lobato, Cheng Zhang ;
Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference, PMLR 118:1-8, 2020.


In this paper, we propose a very simple but e ective VAE model (HM-VAE) that can handle real-valued data with heterogeneous marginals, meaning that they have drastically distinct marginal distributions, statistical properties as well as semantics. Preliminary results show that the HM-VAE can learn distributions with heterogeneous marginal distributions, whereas the vanilla VAEs fails.

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