Preventing Oversmoothing in VAE via Generalized Variance Parameterization
Abstract
Variational autoencoders (VAEs) often suffer from posterior collapse, which is a phenomenon in which the learned latent space becomes uninformative. This is often related to the hyperparameter resembling the data variance. It can be shown that an inappropriate choice of this hyperparameter causes the oversmoothness in the linearly approximated case and can be empirically verified for the general cases. Moreover, determining such appropriate choice becomes infeasible if the data variance is non-uniform or conditional. Therefore, we propose VAE extensions with generalized parameterizations of the data variance and incorporate maximum likelihood estimation into the objective function to adaptively regularize the decoder smoothness. The images generated from proposed VAE extensions show improved Fr\'echet inception distance (FID) on MNIST and CelebA datasets.
Cite
@article{arxiv.2102.08663,
title = {Preventing Oversmoothing in VAE via Generalized Variance Parameterization},
author = {Yuhta Takida and Wei-Hsiang Liao and Chieh-Hsin Lai and Toshimitsu Uesaka and Shusuke Takahashi and Yuki Mitsufuji},
journal= {arXiv preprint arXiv:2102.08663},
year = {2022}
}
Comments
35 pages with 12 figures, accepted for Neurocomputing