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Diverse super-resolution with pretrained deep hiererarchical VAEs. (arXiv:2205.10347v1 [cs.CV])
May 23, 2022, 1:12 a.m. | Jean Prost, Antoine Houdard, Nicolas Papadakis, Andrés Almansa
cs.CV updates on arXiv.org arxiv.org
Image super-resolution is a one-to-many problem, but most deep-learning based
methods only provide one single solution to this problem. In this work, we
tackle the problem of diverse super-resolution by reusing VD-VAE, a
state-of-the art variational autoencoder (VAE). We find that the hierarchical
latent representation learned by VD-VAE naturally separates the image
low-frequency information, encoded in the latent groups at the top of the
hierarchy, from the image high-frequency details, determined by the latent
groups at the bottom of the …
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