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
AI总结：
针对变分自编码器生成样本模糊的问题，本文提出一种基于短时傅里叶变换并强调局部相位相干性的重构损失增强方法，在MNIST数据集上验证了其有效性。
摘要：
变分自编码器（VAEs）是强大的生成模型，然而与替代生成技术的输出相比，其生成的样本存在特征性的模糊。为解决此问题已做出大量研究努力，多项工作聚焦于修改证据下界（ELBO）的重构项。特别是，许多研究尝试在重构损失中增加频域损失。此类损失函数通常采用傅里叶变换，显式地惩罚生成样本中缺乏负责锐利视觉特征的高频分量。本文探索了以往此类方法中尚未被充分理解的方面，并据此提出对重构项的增强。基于推理，我们使用短时傅里叶变换，并强调输入与输出样本间的局部相位相干性。我们在MNIST数据集上通过定性和定量结果展示了所提损失的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
