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Found 10 papers, 3 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.
Generating Unobserved Alternatives with Tower Implicit Model (TIM)
We consider problems where multiple predictions can be considered correct, but only one of them is given as supervision.
Multimodal Shape Completion via IMLE
Shape completion is the problem of completing partial input shapes such as partial scans.
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.
Better Knowledge Retention through Metric Learning
In continual learning, new categories may be introduced over time, and an ideal learning system should perform well on both the original categories and the new categories.
Generating Unobserved Alternatives
This setting differs from both the regression and class-conditional generative modelling settings: in the former, there is a unique observed output for each input, which is provided as supervision; in the latter, there are many observed outputs for each input, and many are provided as supervision.
Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood Estimation
Many tasks in computer vision and graphics fall within the framework of conditional image synthesis.
Super-Resolution via Conditional Implicit Maximum Likelihood Estimation
Single-image super-resolution (SISR) is a canonical problem with diverse applications.
