  The natural world is long-tailed: rare classes are observed orders of
magnitudes less frequently than common ones, leading to highly-imbalanced data
where rare classes can have only handfuls of examples. Learning from few
examples is a known challenge for deep learning based classification
algorithms, and is the focus of the field of low-shot learning. One potential
approach to increase the training data for these rare classes is to augment the
limited real data with synthetic samples. This has been shown to help, but the
domain shift between real and synthetic hinders the approaches' efficacy when
tested on real data.
  We explore the use of image-to-image translation methods to close the domain
gap between synthetic and real imagery for animal species classification in
data collected from camera traps: motion-activated static cameras used to
monitor wildlife. We use low-level feature alignment between source and target
domains to make synthetic data for a rare species generated using a graphics
engine more "realistic". Compared against a system augmented with unaligned
synthetic data, our experiments show a considerable decrease in classification
error rates on a rare species.
