Error bounds for flow matching methods

Journal article

Score-based generative models are a popular class of generative modelling techniques relying on stochastic differential equations (SDE). From their inception, it was realized that it was also possible to perform generation using ordinary differential equations (ODE) rather than SDE. This led to the introduction of the probability flow ODE approach and denoising diffusion implicit models. Flow matching methods have recently further extended these ODE-based approaches and approximate a flow between two arbitrary probability distributions. Previous work derived bounds on the approximation error of diffusion models under the stochastic sampling regime, given assumptions on the L² loss. We present error bounds for the flow matching procedure using fully deterministic sampling, assuming an L² bound on the approximation error and a certain regularity condition on the data distributions.

Authors: Benton, J; Deligiannidis, G; Doucet, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Transactions on Machine Learning Research
• Journal: Transactions on Machine Learning Research
• Publication date: 2024-02-12
• Acceptance date: 2024-02-03
• ISSN: 2835-8856
• Language: English