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。