  Deep neural networks have gained tremendous importance in many computer
vision tasks. However, their power comes at the cost of large amounts of
annotated data required for supervised training. In this work we review and
compare different techniques available in the literature to improve training
results without acquiring additional annotated real-world data. This goal is
mostly achieved by applying annotation-preserving transformations to existing
data or by synthetically creating more data.
