Variational Autoencoders (VAEs) are powerful generative models, however their
generated samples are known to suffer from a characteristic blurriness, as
compared to the outputs of alternative generating techniques. Extensive
research efforts have been made to tackle this problem, and several works have
focused on modifying the reconstruction term of the evidence lower bound
(ELBO). In particular, many have experimented with augmenting the
reconstruction loss with losses in the frequency domain. Such loss functions
usually employ the Fourier transform to explicitly penalise the lack of higher
frequency components in the generated samples, which are responsible for sharp
visual features. In this paper, we explore the aspects of previous such
approaches which aren't well understood, and we propose an augmentation to the
reconstruction term in response to them. Our reasoning leads us to use the
short-time Fourier transform and to emphasise on local phase coherence between
the input and output samples. We illustrate the potential of our proposed loss
on the MNIST dataset by providing both qualitative and quantitative results.

通过修改变分自编码器的重构项，引入傅里叶变换和局部相位一致性的损失函数，解决生成样本模糊性问题，并在 MNIST 数据集上提供了定性和定量结果。

用于去模糊变分自编码器的短时傅里叶变换

Short-Time Fourier Transform for deblurring Variational Autoencoders

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

Explicitly Minimizing the Blur Error of Variational Autoencoders

Learning disentangled representations with variational autoencoders (VAEs) is
often attributed to the regularisation component of the loss. In this work, we
highlight the interaction between data and the reconstruction term of the loss
as the main contributor to disentanglement in VAEs. We note that standardised
benchmark datasets are constructed in ways that are conducive to learning what
appear to be disentangled representations. We design an intuitive adversarial
dataset that exploits this mechanism to break existing state-of-the-art
disentanglement frameworks. Finally, we supply a solution that enables
disentanglement by modifying the reconstruction loss, affecting how VAEs
perceive distances between data points.

本文研究如何通过修改重建损失来实现 VAE 的去纠缠表征，并提出一种广义对抗训练的数据集来破解现有的去纠缠框架。