  Machine learning is driven by data, yet while their availability is
constantly increasing, training data require laborious, time consuming and
error-prone labelling or ground truth acquisition, which in some cases is very
difficult or even impossible. Recent works have resorted to synthetic data
generation, but the inferior performance of models trained on synthetic data
when applied to the real world, introduced the challenge of unsupervised domain
adaptation. In this work we investigate an unsupervised domain adaptation
technique that descends from another perspective, in order to avoid the
complexity of adversarial training and cycle consistencies. We exploit the
recent advances in photorealistic style transfer and take a fully data driven
approach. While this concept is already implicitly formulated within the
intricate objectives of domain adaptation GANs, we take an explicit approach
and apply it directly as data pre-processing. The resulting technique is
scalable, efficient and easy to implement, offers competitive performance to
the complex state-of-the-art alternatives and can open up new pathways for
domain adaptation.
