Papers › Probabilistic Autoencoder
Probabilistic Autoencoder
Vanessa Böhm, Uroš Seljak
Principal Component Analysis (PCA) minimizes the reconstruction error given a class of linear models of fixed component dimensionality. Probabilistic PCA adds a probabilistic structure by learning the probability distribution of the PCA latent space weights, thus creating a generative model. Autoencoders (AE) minimize the reconstruction error in a class of nonlinear models of fixed latent space dimensionality and outperform PCA at fixed dimensionality. Here, we introduce the Probabilistic Autoencoder (PAE) that learns the probability distribution of the AE latent space weights using a normalizing flow (NF). The PAE is fast and easy to train and achieves small reconstruction errors, high sample quality, and good performance in downstream tasks. We compare the PAE to Variational AE (VAE), showing that the PAE trains faster, reaches a lower reconstruction error, and produces good sample quality without requiring special tuning parameters or training procedures. We further demonstrate that the PAE is a powerful model for performing the downstream tasks of probabilistic image reconstruction in the context of Bayesian inference of inverse problems for inpainting and denoising applications. Finally, we identify latent space density from NF as a promising outlier detection metric.
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Code
Syntology Ran 1 of 13 code samples harvested from 2 repositories linked to this paper; 12 have no recorded run. Of those that ran: 1 ran · our draft was wrong.
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Generation | CelebA 64x64 | PAE | FID | 49.2 | #34 of 39 | Archive leaderboard | report |
| Image Generation | Fashion-MNIST | PAE | FID | 28.0 | #4 of 7 | Archive leaderboard | report |
| Out-of-Distribution Detection | Fashion-MNIST | PAE | AUROC | 0.997 | #1 of 2 | Archive leaderboard | report |
| Outlier Detection | Fashion-MNIST | PAE | AUROC | 0.997 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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