Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mining the manifolds of deep generative models for multiple
  data-consistent solutions of ill-posed tomographic imaging problems   Tomographic imaging is in general an ill-posed inverse problem. Typically, a
single regularized image estimate of the sought-after object is obtained from
tomographic measurements. However, there may be multiple objects that are all
consistent with the same measurement data. The ability to generate such
alternate solutions is important because it may enable new assessments of
imaging systems. In principle, this can be achieved by means of posterior
sampling methods. In recent years, deep neural networks have been employed for
posterior sampling with promising results. However, such methods are not yet
for use with large-scale tomographic imaging applications. On the other hand,
empirical sampling methods may be computationally feasible for large-scale
imaging systems and enable uncertainty quantification for practical
applications. Empirical sampling involves solving a regularized inverse problem
within a stochastic optimization framework in order to obtain alternate
data-consistent solutions. In this work, we propose a new empirical sampling
method that computes multiple solutions of a tomographic inverse problem that
are consistent with the same acquired measurement data. The method operates by
repeatedly solving an optimization problem in the latent space of a style-based
generative adversarial network (StyleGAN), and was inspired by the Photo
Upsampling via Latent Space Exploration (PULSE) method that was developed for
super-resolution tasks. The proposed method is demonstrated and analyzed via
numerical studies that involve two stylized tomographic imaging modalities.
These studies establish the ability of the method to perform efficient
empirical sampling and uncertainty quantification.

22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int