Score-based generative models are provably robust: an uncertainty quantification perspective

24 May 2024

Papers citing "Score-based generative models are provably robust: an uncertainty quantification perspective"

8 / 8 papers shown

Title
Wasserstein Convergence of Score-based Generative Models under Semiconvexity and Discontinuous Gradients Stefano Bruno Sotirios Sabanis DiffM 34 0 0 06 May 2025
Beyond Log-Concavity and Score Regularity: Improved Convergence Bounds for Score-Based Generative Models in W2-distance Marta Gentiloni-Silveri Antonio Ocello 28 2 0 04 Jan 2025
Wasserstein proximal operators describe score-based generative models and resolve memorization Benjamin J. Zhang Siting Liu Wuchen Li M. Katsoulakis Stanley J. Osher DiffM 30 8 0 09 Feb 2024
A Good Score Does not Lead to A Good Generative Model Sixu Li Shi Chen Qin Li DiffM 64 15 0 10 Jan 2024
On diffusion-based generative models and their error bounds: The log-concave case with full convergence estimates Stefano Bruno Ying Zhang Dong-Young Lim Ömer Deniz Akyildiz Sotirios Sabanis DiffM 21 4 0 22 Nov 2023
Diffusion Models are Minimax Optimal Distribution Estimators Kazusato Oko Shunta Akiyama Taiji Suzuki DiffM 61 84 0 03 Mar 2023
Convergence of score-based generative modeling for general data distributions Holden Lee Jianfeng Lu Yixin Tan DiffM 177 128 0 26 Sep 2022
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions Sitan Chen Sinho Chewi Jungshian Li Yuanzhi Li Adil Salim Anru R. Zhang DiffM 123 245 0 22 Sep 2022