  Though remarkable progress has been achieved in various vision tasks, deep
neural networks still suffer obvious performance degradation when tested in
out-of-distribution scenarios. We argue that the feature statistics (mean and
standard deviation), which carry the domain characteristics of the training
data, can be properly manipulated to improve the generalization ability of deep
learning models. Common methods often consider the feature statistics as
deterministic values measured from the learned features and do not explicitly
consider the uncertain statistics discrepancy caused by potential domain shifts
during testing. In this paper, we improve the network generalization ability by
modeling the uncertainty of domain shifts with synthesized feature statistics
during training. Specifically, we hypothesize that the feature statistic, after
considering the potential uncertainties, follows a multivariate Gaussian
distribution. Hence, each feature statistic is no longer a deterministic value,
but a probabilistic point with diverse distribution possibilities. With the
uncertain feature statistics, the models can be trained to alleviate the domain
perturbations and achieve better robustness against potential domain shifts.
Our method can be readily integrated into networks without additional
parameters. Extensive experiments demonstrate that our proposed method
consistently improves the network generalization ability on multiple vision
tasks, including image classification, semantic segmentation, and instance
retrieval. The code can be available at https://github.com/lixiaotong97/DSU.
