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.
This paper introduces a theoretical perspective that reinterprets deep generative models as probability transformation functions. It argues that despite diverse architectures, such as autoencoders, GANs, and normalizing flows, these models fundamentally transform simple distributions into complex target data distributions. The paper outlines the main types of generative models, emphasizing the necessity for a unified theoretical framework that could enhance the transfer of knowledge and techniques across different model types. The authors explore different generative approaches, including variational autoencoders and diffusion models, describing their operational principles and showcasing how they fit within the proposed transformation function framework. This comprehensive view aims to bridge knowledge gaps within the field, potentially leading to more efficient generative modeling techniques.
This paper employs the following methods:
- Autoencoders
- Variational Autoencoders
- Generative Adversarial Networks
- Normalizing Flows
- Diffusion Models
- Flow Matching
The following datasets were used in this research:
- A unified perspective on generative models as probability transformation functions.
- Demonstration that different generative models share a common operational principle despite architectural differences.
- Theoretical foundation for developing methodologies that can apply universally across various generative model types.
- Number of GPUs: None specified
- GPU Type: None specified
- Compute Requirements: None specified