  Semantic segmentation models have reached remarkable performance across
various tasks. However, this performance is achieved with extremely large
models, using powerful computational resources and without considering training
and inference time. Real-world applications, on the other hand, necessitate
models with minimal memory demands, efficient inference speed, and executable
with low-resources embedded devices, such as self-driving vehicles. In this
paper, we look at the challenge of real-time semantic segmentation across
domains, and we train a model to act appropriately on real-world data even
though it was trained on a synthetic realm. We employ a new lightweight and
shallow discriminator that was specifically created for this purpose. To the
best of our knowledge, we are the first to present a real-time adversarial
approach for assessing the domain adaption problem in semantic segmentation. We
tested our framework in the two standard protocol: GTA5 to Cityscapes and
SYNTHIA to Cityscapes. Code is available at:
https://github.com/taveraantonio/RTDA.
