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 available 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：为追求鲁棒且泛化能力强的机器学习模型，域泛化（Domain Generalization, DG）在过去几年中受到广泛关注。DG的目标是训练出在遇到与训练数据分布不同的新数据时仍能保持良好性能的模型。尽管深度卷积神经网络（CNN）已在多项下游计算机视觉任务中取得卓越表现，但其在面对未见过的数据域时仍常因泛化能力不足而失效。为此，本研究致力于构建能在数据分布偏移下保持鲁棒的模型，并提出一种针对单源DG图像分类场景中卷积神经网络架构的改进正则化技术。为缓解源域与目标域之间的域偏移问题，我们提出对CNN中间特征图进行增强：具体通过一种新型增强层（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，这是首个在DG图像分类场景下提出此类方法的研究。在PACS、VLCS、Office-Home及TerraIncognita等DG基准数据集上的实验验证了该方法的有效性——我们的模型在多数情况下超越了现有最先进算法。