A Geometric Perspective on Variational Autoencoders. (arXiv:2209.07370v2 [stat.ML] UPDATED)

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 space and we show that using
this scheme can …

arxiv perspective variational autoencoders