Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Computed Tomography Reconstruction Using Deep Image Prior and Learned
  Reconstruction Methods   In this work, we investigate the application of deep learning methods for
computed tomography in the context of having a low-data regime. As motivation,
we review some of the existing approaches and obtain quantitative results after
training them with different amounts of data. We find that the learned
primal-dual has an outstanding performance in terms of reconstruction quality
and data efficiency. However, in general, end-to-end learned methods have two
issues: a) lack of classical guarantees in inverse problems and b) lack of
generalization when not trained with enough data. To overcome these issues, we
bring in the deep image prior approach in combination with classical
regularization. The proposed methods improve the state-of-the-art results in
the low data-regime.

21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph