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arXiv · 2302.04534

Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes

Ba-Hien Tran·Babak Shahbaba·Stephan Mandt·Maurizio Filippone
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Abstract

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.

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Ba-Hien Tran, Babak Shahbaba, Stephan Mandt, Maurizio Filippone. 2023-02-09. Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes. https://arxiv.org/abs/2302.04534

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