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arXiv · 2506.17171

Deep generative models as the probability transformation functions

Vitalii Bondar·Vira Babenko·Roman Trembovetskyi·Yurii Korobeinyk·Viktoriya Dzyuba
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Abstract

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.

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Vitalii Bondar, Vira Babenko, Roman Trembovetskyi, Yurii Korobeinyk, Viktoriya Dzyuba. 2025-06-20. Deep generative models as the probability transformation functions. https://doi.org/10.1007/978-3-032-16808-5_8

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