In search of robust and generalizable machine learning models, Domain Generalization (DG) has gained significant traction during the past few years. The goal in DG is to produce models which continue to perform well when presented with data distributions different from the ones seen during training. While deep convolutional neural networks (CNN) have been able to achieve outstanding performance on downstream computer vision tasks, they still often fail to generalize on previously unseen data Domains. Therefore, in this work we focus on producing a model which is able to remain robust under data distribution shift and propose an alternative regularization technique for convolutional neural network architectures in the single-source DG image classification setting. To mitigate the problem caused by domain shift between source and target data, we propose augmenting intermediate feature maps of CNNs. Specifically, we pass them through a novel Augmentation Layer to prevent models from overfitting on the training set and improve their cross-domain generalization. To the best of our knowledge, this is the first paper proposing such a setup for the DG image classification setting. Experiments on the DG benchmark datasets of PACS, VLCS, Office-Home and TerraIncognita validate the effectiveness of our method, in which our model surpasses state-of-the-art algorithms in most cases.

翻译：在寻求鲁棒且可泛化的机器学习模型的过程中，域泛化（Domain Generalization, DG）在过去几年中获得了广泛关注。DG的目标是构建在遇到与训练时不同的数据分布时仍能表现良好的模型。尽管深度卷积神经网络（CNN）在下游计算机视觉任务中取得了卓越性能，但在面对先前未见过的数据域时，它们常常仍难以实现泛化。因此，本研究聚焦于生成一个能在数据分布偏移下保持鲁棒的模型，并针对单源DG图像分类场景中的卷积神经网络架构提出了一种替代性正则化技术。为缓解源域与目标域之间域偏移所引发的问题，我们提出对CNN的中间特征图进行增强。具体而言，我们通过一种新颖的增强层（Augmentation Layer）处理这些特征图，以防止模型在训练集上过拟合，并提升其跨域泛化能力。据我们所知，这是首篇在DG图像分类场景中提出此类框架的论文。在PACS、VLCS、Office-Home和TerraIncognita等DG基准数据集上的实验验证了本方法的有效性，我们的模型在多数情况下超越了现有最先进算法。