Promising Directions for Future Work

In this concluding chapter, we discuss the next steps that we can expect from the field of synthetic data for deep learning. We consider four different ideas that are starting to gain traction in this field. First, procedural generation of synthetic data can allow for much larger synthetic datasets or datasets generated on the fly. Second, recent works try to make the shift from domain randomization to the generation feedback loop, adapting synthetic data generation to the model and problem at hand. Third, we discuss how to best incorporate additional knowledge into the domain adaptation architectures, and fourth, show examples of introducing extra modalities into synthetic datasets with the purpose to improve downstream tasks that formally might not even use these modalities.