  Using synthetic data for training neural networks that achieve good
performance on real-world data is an important task as it can reduce the need
for costly data annotation. Yet, synthetic and real world data have a domain
gap. Reducing this gap, also known as domain adaptation, has been widely
studied in recent years. Closing the domain gap between the source (synthetic)
and target (real) data by directly performing the adaptation between the two is
challenging. In this work, we propose a novel two-stage framework for improving
domain adaptation techniques on image data. In the first stage, we
progressively train a multi-scale neural network to perform image translation
from the source domain to the target domain. We denote the new transformed data
as "Source in Target" (SiT). Then, we insert the generated SiT data as the
input to any standard UDA approach. This new data has a reduced domain gap from
the desired target domain, which facilitates the applied UDA approach to close
the gap further. We emphasize the effectiveness of our method via a comparison
to other leading UDA and image-to-image translation techniques when used as SiT
generators. Moreover, we demonstrate the improvement of our framework with
three state-of-the-art UDA methods for semantic segmentation, HRDA, DAFormer
and ProDA, on two UDA tasks, GTA5 to Cityscapes and Synthia to Cityscapes.
