Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Regularising Inverse Problems with Generative Machine Learning Models   Deep neural network approaches to inverse imaging problems have produced
impressive results in the last few years. In this paper, we consider the use of
generative models in a variational regularisation approach to inverse problems.
The considered regularisers penalise images that are far from the range of a
generative model that has learned to produce images similar to a training
dataset. We name this family \textit{generative regularisers}. The success of
generative regularisers depends on the quality of the generative model and so
we propose a set of desired criteria to assess models and guide future
research. In our numerical experiments, we evaluate three common generative
models, autoencoders, variational autoencoders and generative adversarial
networks, against our desired criteria. We also test three different generative
regularisers on the inverse problems of deblurring, deconvolution, and
tomography. We show that the success of solutions restricted to lie exactly in
the range of the generator is highly dependent on the ability of the generative
model but that allowing small deviations from the range of the generator
produces more consistent results.

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The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
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The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
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The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an