  Semantic image segmentation is particularly vital for the advancement of
autonomous vehicle technologies. However, this domain faces substantial
challenges under adverse conditions like rain or darkness, which remain
underrepresented in most datasets. The generation of additional training data
for these scenarios is not only costly but also fraught with potential
inaccuracies, largely attributable to the aleatoric uncertainty inherent in
such conditions.
  We introduce BTSeg, an innovative, semi-supervised training approach
enhancing semantic segmentation models in order to effectively handle a range
of adverse conditions without requiring the creation of extensive new datasets.
BTSeg employs a novel application of the Barlow Twins loss, a concept borrowed
from unsupervised learning. The original Barlow Twins approach uses stochastic
augmentations in order to learn useful representations from unlabeled data
without the need for external labels. In our approach, we regard images
captured at identical locations but under varying adverse conditions as
manifold representation of the same scene (which could be interpreted as
"natural augmentations"), thereby enabling the model to conceptualize its
understanding of the environment.
  We evaluate our approach on the ACDC dataset, where it performs favorably
when compared to the current state-of-the-art methods, while also being simpler
to implement and train. For the new challenging ACG benchmark it shows
cutting-edge performance, demonstrating its robustness and generalization
capabilities. We will make the code publicly available post-acceptance.
