no code implementations • 20 Dec 2023 • Shichong Peng, Alireza Moazeni, Ke Li
We assess the validity of these models' outputs as solutions to the inverse problems and conduct a thorough analysis of the reliability of the models' estimates of uncertainty over the solution.
1 code implementation • NeurIPS 2023 • Yanshu Zhang, Shichong Peng, Alireza Moazeni, Ke Li
PAPR effectively learns point cloud positions to represent the correct scene geometry, even when the initialization drastically differs from the target geometry.
1 code implementation • 25 Nov 2022 • Shichong Peng, Alireza Moazeni, Ke Li
A persistent challenge in conditional image synthesis has been to generate diverse output images from the same input image despite only one output image being observed per input image.
no code implementations • 16 Jun 2021 • Shichong Peng, Alireza Moazeni, Ke Li
Deep generative models such as GANs have driven impressive advances in conditional image synthesis in recent years.