CoVAE: correlated multimodal generative modeling
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866911479180034048 |
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| author | Caretti, Federico Sanguinetti, Guido |
| author_facet | Caretti, Federico Sanguinetti, Guido |
| contents | Multimodal Variational Autoencoders have emerged as a popular tool to extract effective representations from rich multimodal data. However, such models rely on fusion strategies in latent space that destroy the joint statistical structure of the multimodal data, with profound implications for generation and uncertainty quantification. In this work, we introduce Correlated Variational Autoencoders (CoVAE), a new generative architecture that captures the correlations between modalities. We test CoVAE on a number of real and synthetic data sets demonstrating both accurate cross-modal reconstruction and effective quantification of the associated uncertainties. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_01965 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CoVAE: correlated multimodal generative modeling Caretti, Federico Sanguinetti, Guido Machine Learning Quantitative Methods Multimodal Variational Autoencoders have emerged as a popular tool to extract effective representations from rich multimodal data. However, such models rely on fusion strategies in latent space that destroy the joint statistical structure of the multimodal data, with profound implications for generation and uncertainty quantification. In this work, we introduce Correlated Variational Autoencoders (CoVAE), a new generative architecture that captures the correlations between modalities. We test CoVAE on a number of real and synthetic data sets demonstrating both accurate cross-modal reconstruction and effective quantification of the associated uncertainties. |
| title | CoVAE: correlated multimodal generative modeling |
| topic | Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2603.01965 |