  Convolutional Neural Networks (CNNs) have demonstrated potential for the
real-time analysis of data from gravitational-wave detector networks for the
specific case of signals from coalescing compact-object binaries such as
black-hole binaries. Unfortunately, training these CNNs requires a precise
model of the target signal; they are therefore not applicable to a wide class
of potential gravitational-wave sources, such as core-collapse supernovae and
long gamma-ray bursts, where unknown physics or computational limitations
prevent the development of comprehensive signal models. We demonstrate for the
first time a CNN with the ability to detect generic signals -- those without a
precise model -- with sensitivity across a wide parameter space. Our CNN has a
novel structure that uses not only the network strain data but also the Pearson
cross-correlation between detectors to distinguish correlated
gravitational-wave signals from uncorrelated noise transients. We demonstrate
the efficacy of our CNN using data from the second LIGO-Virgo observing run,
and show that it has sensitivity comparable to that of the "gold-standard"
transient searches currently used by LIGO-Virgo, at extremely low (order of 1
second) latency and using only a fraction of the computing power required by
existing searches, allowing our models the possibility of true real-time
detection of gravitational-wave transients associated with gamma-ray bursts,
core-collapse supernovae, and other relativistic astrophysical phenomena.
