Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Electromagnetic Source Imaging via a Data-Synthesis-Based Denoising
  Autoencoder   Electromagnetic source imaging (ESI) is a highly ill-posed inverse problem.
To find a unique solution, traditional ESI methods impose a variety of priors
that may not reflect the actual source properties. Such limitations of
traditional ESI methods hinder their further applications. Inspired by deep
learning approaches, a novel data-synthesized spatio-temporal denoising
autoencoder method (DST-DAE) method was proposed to solve the ESI inverse
problem. Unlike the traditional methods, we utilize a neural network to
directly seek generalized mapping from the measured E/MEG signals to the
cortical sources. A novel data synthesis strategy is employed by introducing
the prior information of sources to the generated large-scale samples using the
forward model of ESI. All the generated data are used to drive the neural
network to automatically learn inverse mapping. To achieve better estimation
performance, a denoising autoencoder (DAE) architecture with spatio-temporal
feature extraction blocks is designed. Compared with the traditional methods,
we show (1) that the novel deep learning approach provides an effective and
easy-to-apply way to solve the ESI problem, that (2) compared to traditional
methods, DST-DAE with the data synthesis strategy can better consider the
characteristics of real sources than the mathematical formulation of prior
assumptions, and that (3) the specifically designed architecture of DAE can not
only provide a better estimation of source signals but also be robust to noise
pollution. Extensive numerical experiments show that the proposed method is
superior to the traditional knowledge-driven ESI methods.

11
The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc