  Strong gravitational lenses allow us to peer into the farthest reaches of
space by bending the light from a background object around a massive object in
the foreground. Unfortunately, these lenses are extremely rare, and manually
finding them in astronomy surveys is difficult and time-consuming. We are thus
tasked with finding them in an automated fashion with few if any, known lenses
to form positive samples. To assist us with training, we can simulate realistic
lenses within our survey images to form positive samples. Naively training a
ResNet model with these simulated lenses results in a poor precision for the
desired high recall, because the simulations contain artifacts that are learned
by the model. In this work, we develop a lens detection method that combines
simulation, data augmentation, semi-supervised learning, and GANs to improve
this performance by an order of magnitude. We perform ablation studies and
examine how performance scales with the number of non-lenses and simulated
lenses. These findings allow researchers to go into a survey mostly ``blind"
and still classify strong gravitational lenses with high precision and recall.
