Autoencoders and their variants are among the most widely used models in representation learning and generative modeling. However, autoencoder-based models usually assume that the learned representations are i.i.d. and fail to capture the correlations between the data samples. To address this issue, we propose a novel Sparse Gaussian Process Bayesian Autoencoder (SGPBAE) model in which we impose fully Bayesian sparse Gaussian Process priors on the latent space of a Bayesian Autoencoder. We perform posterior estimation for this model via stochastic gradient Hamiltonian Monte Carlo. We evaluate our approach qualitatively and quantitatively on a wide range of representation learning and generative modeling tasks and show that our approach consistently outperforms multiple alternatives relying on Variational Autoencoders.
@article{arxiv.2302.04534,
title = {Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes},
author = {Ba-Hien Tran and Babak Shahbaba and Stephan Mandt and Maurizio Filippone},
journal= {arXiv preprint arXiv:2302.04534},
year = {2023}
}