  Improving model's generalizability against domain shifts is crucial,
especially for safety-critical applications such as autonomous driving.
Real-world domain styles can vary substantially due to environment changes and
sensor noises, but deep models only know the training domain style. Such domain
style gap impedes model generalization on diverse real-world domains. Our
proposed Normalization Perturbation (NP) can effectively overcome this domain
style overfitting problem. We observe that this problem is mainly caused by the
biased distribution of low-level features learned in shallow CNN layers. Thus,
we propose to perturb the channel statistics of source domain features to
synthesize various latent styles, so that the trained deep model can perceive
diverse potential domains and generalizes well even without observations of
target domain data in training. We further explore the style-sensitive channels
for effective style synthesis. Normalization Perturbation only relies on a
single source domain and is surprisingly effective and extremely easy to
implement. Extensive experiments verify the effectiveness of our method for
generalizing models under real-world domain shifts.
