In domain generalization (DG), the target domain is unknown when the model is
being trained, and the trained model should successfully work on an arbitrary
(and possibly unseen) target domain during inference. This is a difficult
problem, and despite active studies in recent years, it remains a great
challenge. In this paper, we take a simple yet effective approach to tackle
this issue. We propose test-time style shifting, which shifts the style of the
test sample (that has a large style gap with the source domains) to the nearest
source domain that the model is already familiar with, before making the
prediction. This strategy enables the model to handle any target domains with
arbitrary style statistics, without additional model update at test-time.
Additionally, we propose style balancing, which provides a great platform for
maximizing the advantage of test-time style shifting by handling the
DG-specific imbalance issues. The proposed ideas are easy to implement and
successfully work in conjunction with various other DG schemes. Experimental
results on different datasets show the effectiveness of our methods.

本文提出了测试时样式转换和样式均衡方法以解决域泛化中的模型训练与目标域未知的问题。在不需要进一步模型更新的情况下，通过这种方法，模型可以处理任意具有任意样式统计数据的目标域，并且已在不同数据集上得到实验证明其有效性。