Generative Modelling with High-Order Langevin Dynamics

arXiv:2404.12814v1 Announce Type: new
Abstract: Diffusion generative modelling (DGM) based on stochastic
differential equations (SDEs) with
score matching has achieved unprecedented results in data
generation.
In this paper, we propose a novel fast high-quality
generative modelling method
based on high-order
Langevin dynamics (HOLD) with score matching.
This motive is proved by third-order
Langevin dynamics. By augmenting the
previous SDEs, e.g.
variance exploding or variance preserving SDEs
for single-data variable processes, HOLD can simultaneously
model position, velocity, and
acceleration, thereby improving …

abstract arxiv cs.ai cs.cv cs.lg data differential diffusion dynamics generative modelling novel paper quality results stochastic type