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.

本文介绍了一种新的变分自编码器(VAE)的重构项，它特别惩罚生成模糊图像的能力，同时仍然最大化建模分布下的ELBO。在三个不同的数据集上展示了该损失函数的潜力，优于VAE的几种最近提出的重建损失。

显式最小化变分自编码器的模糊误差