Beyond Diagonal Covariance: Flexible Posterior VAEs via Free-Form Injective Flows
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866909632527597568 |
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| author | Sorrenson, Peter Lührs, Lukas Olischläger, Hans Köthe, Ullrich |
| author_facet | Sorrenson, Peter Lührs, Lukas Olischläger, Hans Köthe, Ullrich |
| contents | Variational Autoencoders (VAEs) are powerful generative models widely used for learning interpretable latent spaces, quantifying uncertainty, and compressing data for downstream generative tasks. VAEs typically rely on diagonal Gaussian posteriors due to computational constraints. Using arguments grounded in differential geometry, we demonstrate inherent limitations in the representational capacity of diagonal covariance VAEs, as illustrated by explicit low-dimensional examples. In response, we show that a regularized variant of the recently introduced Free-form Injective Flow (FIF) can be interpreted as a VAE featuring a highly flexible, implicitly defined posterior. Crucially, this regularization yields a posterior equivalent to a full Gaussian covariance distribution, yet maintains computational costs comparable to standard diagonal covariance VAEs. Experiments on image datasets validate our approach, demonstrating that incorporating full covariance substantially improves model likelihood. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_01522 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Diagonal Covariance: Flexible Posterior VAEs via Free-Form Injective Flows Sorrenson, Peter Lührs, Lukas Olischläger, Hans Köthe, Ullrich Machine Learning Variational Autoencoders (VAEs) are powerful generative models widely used for learning interpretable latent spaces, quantifying uncertainty, and compressing data for downstream generative tasks. VAEs typically rely on diagonal Gaussian posteriors due to computational constraints. Using arguments grounded in differential geometry, we demonstrate inherent limitations in the representational capacity of diagonal covariance VAEs, as illustrated by explicit low-dimensional examples. In response, we show that a regularized variant of the recently introduced Free-form Injective Flow (FIF) can be interpreted as a VAE featuring a highly flexible, implicitly defined posterior. Crucially, this regularization yields a posterior equivalent to a full Gaussian covariance distribution, yet maintains computational costs comparable to standard diagonal covariance VAEs. Experiments on image datasets validate our approach, demonstrating that incorporating full covariance substantially improves model likelihood. |
| title | Beyond Diagonal Covariance: Flexible Posterior VAEs via Free-Form Injective Flows |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.01522 |