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A Geometric Perspective on Variational Autoencoders
[article]
2022
arXiv
pre-print
This paper introduces a new interpretation of the Variational Autoencoder framework by taking a fully geometric point of view. We argue that vanilla VAE models unveil naturally a Riemannian structure in their latent space and that taking into consideration those geometrical aspects can lead to better interpolations and an improved generation procedure. This new proposed sampling method consists in sampling from the uniform distribution deriving intrinsically from the learned Riemannian latent
arXiv:2209.07370v2
fatcat:b44pfyiclzegbpiolfqhzw7s2m