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Feb, 2024
离散时间扩散模型的非渐近收敛:新方法和改进速率
Non-asymptotic Convergence of Discrete-time Diffusion Models: New Approach and Improved Rate
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Yuchen Liang, Peizhong Ju, Yingbin Liang, Ness Shroff
TL;DR
去噪扩散模型是一种将噪声转换为数据的强大生成技术,本论文研究了离散时间扩散模型在更大范围的分布上的收敛性保证,并提出了一种加速采样器来提高收敛速度和维度依赖性。
Abstract
The
denoising diffusion model
emerges recently as a powerful generative technique that converts noise into data. Theoretical
convergence guarantee
has been mainly studied for continuous-time diffusion models, and
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