  Generative adversarial networks (GANs) used in domain adaptation tasks have
the ability to generate images that are both realistic and personalized,
transforming an input image while maintaining its identifiable characteristics.
However, they often require a large quantity of training data to produce
high-quality images in a robust way, which limits their usability in cases when
access to data is limited. In our paper, we explore the potential of using
images from a simulated 3D environment to improve a domain adaptation task
carried out by the MUNIT architecture, aiming to use the resulting images to
raise awareness of the potential future impacts of climate change.
