Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Conditional Variational Autoencoder for Learned Image Reconstruction   Learned image reconstruction techniques using deep neural networks have
recently gained popularity, and have delivered promising empirical results.
However, most approaches focus on one single recovery for each observation, and
thus neglect the uncertainty information. In this work, we develop a novel
computational framework that approximates the posterior distribution of the
unknown image at each query observation. The proposed framework is very
flexible: It handles implicit noise models and priors, it incorporates the data
formation process (i.e., the forward operator), and the learned reconstructive
properties are transferable between different datasets. Once the network is
trained using the conditional variational autoencoder loss, it provides a
computationally efficient sampler for the approximate posterior distribution
via feed-forward propagation, and the summarizing statistics of the generated
samples are used for both point-estimation and uncertainty quantification. We
illustrate the proposed framework with extensive numerical experiments on
positron emission tomography (with both moderate and low count levels) showing
that the framework generates high-quality samples when compared with
state-of-the-art methods.

8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th