论文标题
更简单是更好的：频谱正则化和变化自动编码器的上采样技术
Simpler is better: spectral regularization and up-sampling techniques for variational autoencoders
论文作者
论文摘要
基于神经网络的生成模型的光谱行为的完整表征仍然是一个空旷的问题。最近的研究重点关注生成的对抗网络以及真实图像和生成图像之间的高频差异。避免这种情况的当前解决方案是用双线性升压替换转置卷积，或者在发电机中添加光谱正则化项。众所周知，各种自动编码器（VAE）也遭受了这些问题的困扰。在这项工作中，我们提出了VAE的简单2D傅立叶基于转换的光谱正则化损失，并表明它可以实现等于或更好的结果，而不是当前的生成模型频率吸引损失。此外，我们尝试改变发电机网络中的上采样过程，并研究它如何影响模型的光谱性能。我们包括有关合成和实际数据集的实验，以证明我们的结果。
Full characterization of the spectral behavior of generative models based on neural networks remains an open issue. Recent research has focused heavily on generative adversarial networks and the high-frequency discrepancies between real and generated images. The current solution to avoid this is to either replace transposed convolutions with bilinear up-sampling or add a spectral regularization term in the generator. It is well known that Variational Autoencoders (VAEs) also suffer from these issues. In this work, we propose a simple 2D Fourier transform-based spectral regularization loss for the VAE and show that it can achieve results equal to, or better than, the current state-of-the-art in frequency-aware losses for generative models. In addition, we experiment with altering the up-sampling procedure in the generator network and investigate how it influences the spectral performance of the model. We include experiments on synthetic and real data sets to demonstrate our results.
