Deep generative models as the probability transformation functions
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913178380664832 |
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| author | Bondar, Vitalii Babenko, Vira Trembovetskyi, Roman Korobeinyk, Yurii Dzyuba, Viktoriya |
| author_facet | Bondar, Vitalii Babenko, Vira Trembovetskyi, Roman Korobeinyk, Yurii Dzyuba, Viktoriya |
| contents | This paper introduces a unified theoretical perspective that views deep generative models as probability transformation functions. Despite the apparent differences in architecture and training methodologies among various types of generative models - autoencoders, autoregressive models, generative adversarial networks, normalizing flows, diffusion models, and flow matching - we demonstrate that they all fundamentally operate by transforming simple predefined distributions into complex target data distributions. This unifying perspective facilitates the transfer of methodological improvements between model architectures and provides a foundation for developing universal theoretical approaches, potentially leading to more efficient and effective generative modeling techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17171 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep generative models as the probability transformation functions Bondar, Vitalii Babenko, Vira Trembovetskyi, Roman Korobeinyk, Yurii Dzyuba, Viktoriya Machine Learning 68T07 This paper introduces a unified theoretical perspective that views deep generative models as probability transformation functions. Despite the apparent differences in architecture and training methodologies among various types of generative models - autoencoders, autoregressive models, generative adversarial networks, normalizing flows, diffusion models, and flow matching - we demonstrate that they all fundamentally operate by transforming simple predefined distributions into complex target data distributions. This unifying perspective facilitates the transfer of methodological improvements between model architectures and provides a foundation for developing universal theoretical approaches, potentially leading to more efficient and effective generative modeling techniques. |
| title | Deep generative models as the probability transformation functions |
| topic | Machine Learning 68T07 |
| url | https://arxiv.org/abs/2506.17171 |