# Variational Autoencoders with Riemannian Brownian Motion Priors

@article{Kalatzis2020VariationalAW, title={Variational Autoencoders with Riemannian Brownian Motion Priors}, author={Dimitris Kalatzis and David Eklund and Georgios Arvanitidis and S{\o}ren Hauberg}, journal={ArXiv}, year={2020}, volume={abs/2002.05227} }

Variational Autoencoders (VAEs) represent the given data in a low-dimensional latent space, which is generally assumed to be Euclidean. This assumption naturally leads to the common choice of a standard Gaussian prior over continuous latent variables. Recent work has, however, shown that this prior has a detrimental effect on model capacity, leading to subpar performance. We propose that the Euclidean assumption lies at the heart of this failure mode. To counter this, we assume a Riemannian…

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