Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Effectiveness of Data Augmentation in Cellular-based Localization Using
  Deep Learning   Recently, deep learning-based positioning systems have gained attention due
to their higher performance relative to traditional methods. However, obtaining
the expected performance of deep learning-based systems requires large amounts
of data to train model. Obtaining this data is usually a tedious process which
hinders the utilization of such deep learning approaches. In this paper, we
introduce a number of techniques for addressing the data collection problem for
deep learning-based cellular localization systems. The basic idea is to
generate synthetic data that reflects the typical pattern of the wireless data
as observed from a small collected dataset. Evaluation of the proposed data
augmentation techniques using different Android phones in a cellular
localization case study shows that we can enhance the performance of the
localization systems in both indoor and outdoor scenarios by 157% and 50.5%,
respectively. This highlights the promise of the proposed techniques for
enabling deep learning-based localization systems.

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The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
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The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics