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Found 4 papers, 2 papers with code
How Good Are Deep Generative Models for Solving Inverse Problems?
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.
PAPR: Proximity Attention Point Rendering
PAPR effectively learns point cloud positions to represent the correct scene geometry, even when the initialization drastically differs from the target geometry.
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis
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.
Cascading Modular Network (CAM-Net) for Multimodal Image Synthesis
Deep generative models such as GANs have driven impressive advances in conditional image synthesis in recent years.
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