Deep generative models as the probability transformation functions

• URL: http://arxiv.org/abs/2506.17171v1
• Date: Fri, 20 Jun 2025 17:22:23 GMT
• Title: Deep generative models as the probability transformation functions
• Authors: Vitalii Bondar, Vira Babenko, Roman Trembovetskyi, Yurii Korobeinyk, Viktoriya Dzyuba,
• Abstract summary: This paper introduces a unified theoretical perspective that views deep generative models as probability transformation functions.<n>We demonstrate that they all fundamentally operate by transforming simple predefined distributions into complex target data distributions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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