  Deep learning has made significant advancements in supervised learning.
However, models trained in this setting often face challenges due to domain
shift between training and test sets, resulting in a significant drop in
performance during testing. To address this issue, several domain
generalization methods have been developed to learn robust and domain-invariant
features from multiple training domains that can generalize well to unseen test
domains. Data augmentation plays a crucial role in achieving this goal by
enhancing the diversity of the training data. In this paper, inspired by the
observation that normalizing an image with different statistics generated by
different batches with various domains can perturb its feature, we propose a
simple yet effective method called NormAUG (Normalization-guided Augmentation).
Our method includes two paths: the main path and the auxiliary (augmented)
path. During training, the auxiliary path includes multiple sub-paths, each
corresponding to batch normalization for a single domain or a random
combination of multiple domains. This introduces diverse information at the
feature level and improves the generalization of the main path. Moreover, our
NormAUG method effectively reduces the existing upper boundary for
generalization based on theoretical perspectives. During the test stage, we
leverage an ensemble strategy to combine the predictions from the auxiliary
path of our model, further boosting performance. Extensive experiments are
conducted on multiple benchmark datasets to validate the effectiveness of our
proposed method.
