Analyzing Neural Network-Based Generative Diffusion Models through Convex Optimization
CoRR(2024)
摘要
Diffusion models are becoming widely used in state-of-the-art image, video
and audio generation. Score-based diffusion models stand out among these
methods, necessitating the estimation of score function of the input data
distribution. In this study, we present a theoretical framework to analyze
two-layer neural network-based diffusion models by reframing score matching and
denoising score matching as convex optimization. Though existing diffusion
theory is mainly asymptotic, we characterize the exact predicted score function
and establish the convergence result for neural network-based diffusion models
with finite data. This work contributes to understanding what neural
network-based diffusion model learns in non-asymptotic settings.
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