  The noise of gravitational-wave (GW) interferometers limits their sensitivity
and impacts the data quality, hindering the detection of GW signals from
astrophysical sources. For transient searches, the most problematic are
transient noise artifacts, known as glitches, that happen at a rate around $ 1
\text{ min}^{-1}$, and can mimic GW signals. Because of this, there is a need
for better modeling and inclusion of glitches in large-scale studies, such as
stress testing the pipelines. In this proof-of concept work we employ
Generative Adversarial Networks (GAN), a state-of-the-art Deep Learning
algorithm inspired by Game Theory, to learn the underlying distribution of blip
glitches and to generate artificial populations. We reconstruct the glitch in
the time-domain, providing a smooth input that the GAN can learn. With this
methodology, we can create distributions of $\sim 10^{3}$ glitches from Hanford
and Livingston detectors in less than one second. Furthermore, we employ
several metrics to measure the performance of our methodology and the quality
of its generations. This investigation will be extended in the future to
different glitch classes with the final goal of creating an open-source
interface for mock data generation.
