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Computer Science > Machine Learning

arXiv:2406.05396 (cs)
[Submitted on 8 Jun 2024]

Title:Mean-field Chaos Diffusion Models

Authors:Sungwoo Park, Dongjun Kim, Ahmed Alaa
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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.
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2406.05396 [cs.LG]
  (or arXiv:2406.05396v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2406.05396

Submission history

From: Ahmed Alaa [view email]
[v1] Sat, 8 Jun 2024 08:24:06 UTC (2,025 KB)
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