  By now, tens of gravitational-wave (GW) events have been detected by the LIGO
and Virgo detectors. These GWs have all been emitted by compact binary
coalescence, for which we have excellent predictive models. However, there
might be other sources for which we do not have reliable models. Some are
expected to exist but to be very rare (e.g., supernovae), while others may be
totally unanticipated. So far, no unmodeled sources have been discovered, but
the lack of models makes the search for such sources much more difficult and
less sensitive. We present here a search for unmodeled GW signals using
semi-supervised machine learning. We apply deep learning and outlier detection
algorithms to labeled spectrograms of GW strain data, and then search for
spectrograms with anomalous patterns in public LIGO data. We searched $\sim
13\%$ of the coincident data from the first two observing runs. No candidates
of GW signals were detected in the data analyzed. We evaluate the sensitivity
of the search using simulated signals, we show that this search can detect
spectrograms containing unusual or unexpected GW patterns, and we report the
waveforms and amplitudes for which a $50\%$ detection rate is achieved.
