  The framework of variational autoencoders (VAEs) provides a principled method
for jointly learning latent-variable models and corresponding inference models.
However, the main drawback of this approach is the blurriness of the generated
images. Some studies link this effect to the objective function, namely, the
(negative) log-likelihood. Here, we propose to enhance VAEs by adding a random
variable that is a downscaled version of the original image and still use the
log-likelihood function as the learning objective. Further, by providing the
downscaled image as an input to the decoder, it can be used in a manner similar
to the super-resolution. We present empirically that the proposed approach
performs comparably to VAEs in terms of the negative log-likelihood, but it
obtains a better FID score in data synthesis.
