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Variational autoencdoers (VAE) are a popular approach to generativemodelling. However, exploiting the capabilities of VAEs in practice can bedifficult. Recent work on regularised and entropic autoencoders have begun toexplore the potential, for generative modelling, of removing the variationalapproach and returning to the classic deterministic autoencoder (DAE) withadditional novel regularisation methods. In this paper we empirically explorethe capability of DAEs for image generation without additional novel methodsand the effect of the implicit regularisation and smoothness of large networks.We find that DAEs can be used successfully for image generation withoutadditional loss terms, and that many of the useful properties of VAEs can ariseimplicitly from sufficiently large convolutional encoders and decoders whentrained on CIFAR-10 and CelebA.

Variational Autoencoders Without the Variation