Nonequilbrium physics of generative diffusion models
<|reference_start|>Nonequilbrium physics of generative diffusion models: Generative diffusion models apply the concept of Langevin dynamics in physics to machine leaning, attracting a lot of interests from engineering, statistics and physics, but a complete picture about inherent mechanisms is still lacking. In this paper, we provide a transparent physics analysis of diffusion models, formulating the fluctuation theorem, entropy production, equilibrium measure, and Franz-Parisi potential to understand the dynamic process and intrinsic phase transitions. Our analysis is rooted in a path integral representation of both forward and backward dynamics, and in treating the reverse diffusion generative process as a statistical inference, where the time-dependent state variables serve as quenched disorder akin to that in spin glass theory. Our study thus links stochastic thermodynamics, statistical inference and geometry based analysis together to yield a coherent picture about how the generative diffusion models work.<|reference_end|>
@article{yu2024nonequilbrium,
    title={Nonequilbrium physics of generative diffusion models},
    author={Zhendong Yu and Haiping Huang},
    journal={arXiv preprint arXiv:2405.11932},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11932},
    primaryClass={cond-mat.stat-mech cond-mat.dis-nn cs.LG}
}