Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sparse aNETT for Solving Inverse Problems with Deep Learning   We propose a sparse reconstruction framework (aNETT) for solving inverse
problems. Opposed to existing sparse reconstruction techniques that are based
on linear sparsifying transforms, we train an autoencoder network $D \circ E$
with $E$ acting as a nonlinear sparsifying transform and minimize a Tikhonov
functional with learned regularizer formed by the $\ell^q$-norm of the encoder
coefficients and a penalty for the distance to the data manifold. We propose a
strategy for training an autoencoder based on a sample set of the underlying
image class such that the autoencoder is independent of the forward operator
and is subsequently adapted to the specific forward model. Numerical results
are presented for sparse view CT, which clearly demonstrate the feasibility,
robustness and the improved generalization capability and stability of aNETT
over post-processing networks.

36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
16
The categories of this paper are: stat
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph