  Variational autoencoders (VAEs) are powerful generative modelling methods,
however they suffer from blurry generated samples and reconstructions compared
to the images they have been trained on. Significant research effort has been
spent to increase the generative capabilities by creating more flexible models
but often flexibility comes at the cost of higher complexity and computational
cost. Several works have focused on altering the reconstruction term of the
evidence lower bound (ELBO), however, often at the expense of losing the
mathematical link to maximizing the likelihood of the samples under the modeled
distribution. Here we propose a new formulation of the reconstruction term for
the VAE that specifically penalizes the generation of blurry images while at
the same time still maximizing the ELBO under the modeled distribution. We show
the potential of the proposed loss on three different data sets, where it
outperforms several recently proposed reconstruction losses for VAEs.

Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing