论文标题
没有变化的变异自动编码器
Variational Autoencoders Without the Variation
论文作者
论文摘要
变异汽车（VAE）是一种流行的生成建模方法。但是，在实践中利用VAE的功能可能很困难。关于正规化和熵自动编码器的最新工作已经开始探索用于生成建模的潜力，以消除变异方法并返回经典的确定性自动码编码器（DAE），并使用其他新颖的正则方法。在本文中，我们从经验上探讨了DAE对于图像生成的能力，而无需其他新颖的方法以及大型网络的隐式正则化和平滑度的效果。我们发现，DAE可以成功地用于图像生成，而无需其他损失项，并且在接受CIFAR-10和CELEBA培训时，VAE的许多有用属性可能会隐含来自足够大的卷积编码器和解码器。
Variational autoencdoers (VAE) are a popular approach to generative modelling. However, exploiting the capabilities of VAEs in practice can be difficult. Recent work on regularised and entropic autoencoders have begun to explore the potential, for generative modelling, of removing the variational approach and returning to the classic deterministic autoencoder (DAE) with additional novel regularisation methods. In this paper we empirically explore the capability of DAEs for image generation without additional novel methods and the effect of the implicit regularisation and smoothness of large networks. We find that DAEs can be used successfully for image generation without additional loss terms, and that many of the useful properties of VAEs can arise implicitly from sufficiently large convolutional encoders and decoders when trained on CIFAR-10 and CelebA.
