  Subtracting event samples is a common task in LHC simulation and analysis,
and standard solutions tend to be inefficient. We employ generative adversarial
networks to produce new event samples with a phase space distribution
corresponding to added or subtracted input samples. We first illustrate for a
toy example how such a network beats the statistical limitations of the
training data. We then show how such a network can be used to subtract
background events or to include non-local collinear subtraction events at the
level of unweighted 4-vector events.
