We propose a simple but effective multi-source domain generalization
technique based on deep neural networks by incorporating optimized
normalization layers that are specific to individual domains. Our approach
employs multiple normalization methods while learning separate affine
parameters per domain. For each domain, the activations are normalized by a
weighted average of multiple normalization statistics. The normalization
statistics are kept track of separately for each normalization type if
necessary. Specifically, we employ batch and instance normalizations in our
implementation to identify the best combination of these two normalization
methods in each domain. The optimized normalization layers are effective to
enhance the generalizability of the learned model. We demonstrate the
state-of-the-art accuracy of our algorithm in the standard domain
generalization benchmarks, as well as viability to further tasks such as
multi-source domain adaptation and domain generalization in the presence of
label noise.

本研究提出了利用深度神经网络的多源域泛化技术，通过引入特定于各个领域的优化归一化层。在每个域中，根据多个归一化统计的加权平均值对激活值进行归一化处理。并且在实现中使用批和实例归一化，以确定这两种归一化方法的最佳组合，优化学习模型的通用性。本方法在标准域泛化基准测试中展示了当前最高的准确率，同时适用于诸如多源领域适应和带有标签噪声的域泛化等进一步的任务。