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Found 7 papers, 5 papers with code
Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction
The solution of inverse problems is of fundamental interest in medical and astronomical imaging, geophysics as well as engineering and life sciences.
Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel
We propose conditional flows of the maximum mean discrepancy (MMD) with the negative distance kernel for posterior sampling and conditional generative modeling.
Generative Sliced MMD Flows with Riesz Kernels
We prove that the MMD of Riesz kernels, which is also known as energy distance, coincides with the MMD of their sliced version.
Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems
Conditional generative models became a very powerful tool to sample from Bayesian inverse problem posteriors.
Neural Wasserstein Gradient Flows for Maximum Mean Discrepancies with Riesz Kernels
Wasserstein gradient flows of maximum mean discrepancy (MMD) functionals with non-smooth Riesz kernels show a rich structure as singular measures can become absolutely continuous ones and conversely.
PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization
Learning neural networks using only few available information is an important ongoing research topic with tremendous potential for applications.
WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution
Exploiting image patches instead of whole images have proved to be a powerful approach to tackle various problems in image processing.
