  Semantic segmentation methods have advanced significantly. Still, their
robustness to real-world perturbations and object types not seen during
training remains a challenge, particularly in safety-critical applications. We
propose a novel approach to improve the robustness of semantic segmentation
techniques by leveraging the synergy between label-to-image generators and
image-to-label segmentation models. Specifically, we design Robusta, a novel
robust conditional generative adversarial network to generate realistic and
plausible perturbed images that can be used to train reliable segmentation
models. We conduct in-depth studies of the proposed generative model, assess
the performance and robustness of the downstream segmentation network, and
demonstrate that our approach can significantly enhance the robustness in the
face of real-world perturbations, distribution shifts, and out-of-distribution
samples. Our results suggest that this approach could be valuable in
safety-critical applications, where the reliability of perception modules such
as semantic segmentation is of utmost importance and comes with a limited
computational budget in inference. We release our code at
https://github.com/ENSTA-U2IS-AI/robusta.
