Our environment is riddled with sensory stimuli that are noisy, ambiguous, and often incomplete, necessitating organisms to handle uncertainty in their sensory observations.

We introduce the vine copula autoencoder (VCAE), a flexible generative model for high-dimensional distributions built in a straightforward three-step procedure.

Our environment is riddled with sensory stimuli that are noisy, ambiguous, and often incomplete, necessitating organisms to handle uncertainty in their sensory observations.

This paper proposes a vine copula autoencoder to construct flexible generative models for high-dimensional, structured data in three steps. By exploiting vine copulas, the proposed approach can transform any already trained autoencoder into a flexible generative model at a low computational cost, and its good performance was nicely demonstrated. This is a nice contribution to the field of constructing deep generative models.

We introduce the vine copula autoencoder (VCAE), a flexible generative model for high-dimensional distributions built in a straightforward three-step procedure. 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.

We introduce the vine copula autoencoder (VCAE), a flexible generative model for high-dimensional distributions built in a straightforward three-step procedure. 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.