  Enhancing the generalization capability of deep neural networks to unseen
domains is crucial for safety-critical applications in the real world such as
autonomous driving. To address this issue, this paper proposes a novel instance
selective whitening loss to improve the robustness of the segmentation networks
for unseen domains. Our approach disentangles the domain-specific style and
domain-invariant content encoded in higher-order statistics (i.e., feature
covariance) of the feature representations and selectively removes only the
style information causing domain shift. As shown in Fig. 1, our method provides
reasonable predictions for (a) low-illuminated, (b) rainy, and (c) unseen
structures. These types of images are not included in the training dataset,
where the baseline shows a significant performance drop, contrary to ours.
Being simple yet effective, our approach improves the robustness of various
backbone networks without additional computational cost. We conduct extensive
experiments in urban-scene segmentation and show the superiority of our
approach to existing work. Our code is available at
https://github.com/shachoi/RobustNet.
