Skip to yearly menu bar Skip to main content


Poster

Mean-field Chaos Diffusion Models

Sungwoo Park · Dongjun Kim · Ahmed Alaa

Hall C 4-9
[ ]
Wed 24 Jul 2:30 a.m. PDT — 4 a.m. PDT
 
Oral presentation: Oral 3x Diffusion Models
Wed 24 Jul 1:30 a.m. PDT — 2:30 a.m. PDT

Abstract:

In this paper, we introduce a new class of score-based generative models (SGMs) designed to handle high-cardinality data distributions by leveraging concepts from mean-field theory. We present mean-field chaos diffusion models (MF-CDMs), which address the curse of dimensionality inherent in high-cardinality data by utilizing the propagation of chaos property of interacting particles. By treating high-cardinality data as a large stochastic system of interacting particles, we develop a novel score-matching method for infinite-dimensional chaotic particle systems and propose an approximation scheme that employs a subdivision strategy for efficient training. Our theoretical and empirical results demonstrate the scalability and effectiveness of MF-CDMs for managing large high-cardinality data structures, such as 3D point clouds.

Live content is unavailable. Log in and register to view live content