4 papers
SFBD-OMNI: Bridge models for lossy measurement restoration with limited clean samples
Haoye Lu, Yaoliang Yu, Darren Lo
In many real-world scenarios, obtaining fully observed samples is prohibitively expensive or even infeasible, while partial and noisy observations are comparatively easy to collect…
SFBD Flow: A Continuous-Optimization Framework for Training Diffusion Models with Noisy Samples
Haoye Lu, Darren Lo, Yaoliang Yu
Diffusion models achieve strong generative performance but often rely on large datasets that may include sensitive content. This challenge is compounded by the models' tendency to…
Stochastic Forward-Backward Deconvolution: Training Diffusion Models with Finite Noisy Datasets
Haoye Lu, Qifan Wu, Yaoliang Yu
Recent diffusion-based generative models achieve remarkable results by training on massive datasets, yet this practice raises concerns about memorization and copyright infringement…
Structure Preserving Diffusion Models
Haoye Lu, Spencer Szabados, Yaoliang Yu
In recent years, diffusion models have become the leading approach for distribution learning. This paper focuses on structure-preserving diffusion models (SPDM), a specific subset…