  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.
