  Semantic segmentation algorithms require access to well-annotated datasets
captured under diverse illumination conditions to ensure consistent
performance. However, poor visibility conditions at varying illumination
conditions result in laborious and error-prone labeling. Alternatively, using
synthetic samples to train segmentation algorithms has gained interest with the
drawback of domain gap that results in sub-optimal performance. While current
state-of-the-art (SoTA) have proposed different mechanisms to bridge the domain
gap, they still perform poorly in low illumination conditions with an average
performance drop of - 10.7 mIOU. In this paper, we focus upon single source
domain generalization to overcome the domain gap and propose a two-step
framework wherein we first identify an adversarial style that maximizes the
domain gap between stylized and source images. Subsequently, these stylized
images are used to categorically align features such that features belonging to
the same class are clustered together in latent space, irrespective of domain
gap. Furthermore, to increase intra-class variance while training, we propose a
style mixing mechanism wherein the same objects from different styles are mixed
to construct a new training image. This framework allows us to achieve a domain
generalized semantic segmentation algorithm with consistent performance without
prior information of the target domain while relying on a single source. Based
on extensive experiments, we match SoTA performance on SYNTHIA $\to$
Cityscapes, GTAV $\to$ Cityscapes while setting new SoTA on GTAV $\to$ Dark
Zurich and GTAV $\to$ Night Driving benchmarks without retraining.
