Uncertainty Visualization via Low-Dimensional Posterior Projections

arXiv:2312.07804v2 Announce Type: replace
Abstract: In ill-posed inverse problems, it is commonly desirable to obtain insight into the full spectrum of plausible solutions, rather than extracting only a single reconstruction. Information about the plausible solutions and their likelihoods is encoded in the posterior distribution. However, for high-dimensional data, this distribution is challenging to visualize. In this work, we introduce a new approach for estimating and visualizing posteriors by employing energy-based models (EBMs) over low-dimensional subspaces. Specifically, we train a conditional …

abstract arxiv cs.cv data distribution however information insight low posterior replace solutions spectrum type uncertainty via visualization