Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast Computational Ghost Imaging using Unpaired Deep Learning and a
  Constrained Generative Adversarial Network   The unpaired training can be the only option available for fast deep
learning-based ghost imaging, where obtaining a high signal-to-noise ratio
(SNR) image copy of each low SNR ghost image could be practically
time-consuming and challenging. This paper explores the capabilities of deep
learning to leverage computational ghost imaging when there is a lack of paired
training images. The deep learning approach proposed here enables fast ghost
imaging through reconstruction of high SNR images from faint and hastily shot
ghost images using a constrained Wasserstein generative adversarial network. In
the proposed approach, the objective function is regularized to enforce the
generation of faithful and relevant high SNR images to the ghost copies. This
regularization measures the distance between reconstructed images and the faint
ghost images in a low-noise manifold generated by a shadow network. The
performance of the constrained network is shown to be particularly important
for ghost images with low SNR. The proposed pipeline is able to reconstruct
high-quality images from the ghost images with SNR values not necessarily equal
to the SNR of the training set.

7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio