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Found 13 papers, 8 papers with code
Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching
In inverse problems, many conditional generative models approximate the posterior measure by minimizing a distance between the joint measure and its learned approximation.
Mixed Noise and Posterior Estimation with Conditional DeepGEM
Motivated by indirect measurements and applications from nanometrology with a mixed noise model, we develop a novel algorithm for jointly estimating the posterior and the noise parameters in Bayesian inverse problems.
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.
Y-Diagonal Couplings: Approximating Posteriors with Conditional Wasserstein Distances
In inverse problems, many conditional generative models approximate the posterior measure by minimizing a distance between the joint measure and its learned approximation.
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.
Multilevel Diffusion: Infinite Dimensional Score-Based Diffusion Models for Image Generation
We thereby intend to obtain diffusion models that generalize across different resolution levels and improve the efficiency of the training process.
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.
Generalized Normalizing Flows via Markov Chains
Normalizing flows, diffusion normalizing flows and variational autoencoders are powerful generative models.
Stochastic Normalizing Flows for Inverse Problems: a Markov Chains Viewpoint
To overcome topological constraints and improve the expressiveness of normalizing flow architectures, Wu, K\"ohler and No\'e introduced stochastic normalizing flows which combine deterministic, learnable flow transformations with stochastic sampling methods.
Invertible Neural Networks versus MCMC for Posterior Reconstruction in Grazing Incidence X-Ray Fluorescence
Grazing incidence X-ray fluorescence is a non-destructive technique for analyzing the geometry and compositional parameters of nanostructures appearing e. g. in computer chips.
Stabilizing Invertible Neural Networks Using Mixture Models
In this paper, we analyze the properties of invertible neural networks, which provide a way of solving inverse problems.
