  The generalization with respect to domain shifts, as they frequently appear
in applications such as autonomous driving, is one of the remaining big
challenges for deep learning models. Therefore, we propose an intra-source
style augmentation (ISSA) method to improve domain generalization in semantic
segmentation. Our method is based on a novel masked noise encoder for StyleGAN2
inversion. The model learns to faithfully reconstruct the image preserving its
semantic layout through noise prediction. Random masking of the estimated noise
enables the style mixing capability of our model, i.e. it allows to alter the
global appearance without affecting the semantic layout of an image. Using the
proposed masked noise encoder to randomize style and content combinations in
the training set, ISSA effectively increases the diversity of training data and
reduces spurious correlation. As a result, we achieve up to $12.4\%$ mIoU
improvements on driving-scene semantic segmentation under different types of
data shifts, i.e., changing geographic locations, adverse weather conditions,
and day to night. ISSA is model-agnostic and straightforwardly applicable with
CNNs and Transformers. It is also complementary to other domain generalization
techniques, e.g., it improves the recent state-of-the-art solution RobustNet by
$3\%$ mIoU in Cityscapes to Dark Z\"urich.
