  Transient noise glitches in gravitational-wave detector data limit the
sensitivity of searches and contaminate detected signals. In this Paper, we
show how glitches can be simulated using generative adversarial networks. We
produce hundreds of synthetic images for the 22 most common types of glitches
seen in the LIGO, KAGRA, and Virgo detectors. The artificial glitches can be
used to improve the performance of searches and parameter-estimation
algorithms. We perform a neural network classification to show that our
artificial glitches are an excellent match for real glitches, with an average
classification accuracy across all 22 glitch types of 99.0%.
