arXiv:2606.01645v1 Announce Type: cross Abstract: Diffusion models have emerged as a leading framework for deep generative modeling. While the standard Gaussian formulation is theoretically convenient, its suitability for heavy-tailed datasets remains unclear. To address this, heavy-tailed diffusion models (HTDMs) extend the standard formulation by replacing the Gaussian distribution with a Student's t-distribution, thereby improving tail fidelity on heavy-tailed datasets. Although stochastic differential equation (SDE)-based sampling is possible in HTDMs, it has not been fully explored. In th
The paper addresses a current limitation in diffusion models, their suitability for heavy-tailed data, indicating active research into improving their robustness and applicability.
Improving diffusion models' ability to handle diverse data distributions expands their potential applications, particularly in complex, real-world generative AI tasks.
This research suggests a refinement in the foundational mathematics of diffusion models, leading to more robust and versatile generative AI, especially for datasets with unusual or extreme values.
- · AI researchers
- · Deep generative modeling
- · Industries with heavy-tailed data (e.g., finance, atmospheric science)
- · Diffusion models solely reliant on Gaussian formulations
Heavy-tailed diffusion models achieve better performance on specific datasets due to improved theoretical underpinnings.
The enhanced fidelity opens up new applications for generative AI in domains previously challenging for standard diffusion models.
More robust generative AI capabilities could accelerate novel content creation and data synthesis across various industries, impacting product design and data augmentation strategies.
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Read at arXiv cs.LG