We introduce the vine copula autoencoder (VCAE), a flexible generative model
for high-dimensional distributions built in a straightforward three-step procedure.
First, an autoencoder (AE) compresses the data into a lower dimensional representation. Second, the multivariate distribution of the encoded data is estimated with
vine copulas. Third, a generative model is obtained by combining the estimated
distribution with the decoder part of the AE. As such, the proposed approach
can transform any already trained AE into a flexible generative model at a low
computational cost. This is an advantage over existing generative models such as
adversarial networks and variational AEs which can be difficult to train and can
impose strong assumptions on the latent space. Experiments on MNIST, Street
View House Numbers and Large-Scale CelebFaces Attributes datasets show that
VCAEs can achieve competitive results to standard baselines.