Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Applying Generative Adversarial Networks to Intelligent Subsurface
  Imaging and Identification   To augment training data for machine learning models in Ground Penetrating
Radar (GPR) data classification and identification, this thesis focuses on the
generation of realistic GPR data using Generative Adversarial Networks. An
innovative GAN architecture is proposed for generating GPR B-scans, which is,
to the author's knowledge, the first successful application of GAN to GPR
B-scans. As one of the major contributions, a novel loss function is formulated
by merging frequency domain with time domain features. To test the efficacy of
generated B-scans, a real time object classifier is proposed to measure the
performance gain derived from augmented B-Scan images. The numerical experiment
illustrated that, based on the augmented training data, the proposed GAN
architecture demonstrated a significant increase (from 82% to 98%) in the
accuracy of the object classifier.

2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat