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.

本文提出了一种基于卷积神经网络的交叉域泛化图像分类方法，通过 Augmentation Layer 增强 CNN 的中间特征图以提高模型在跨域数据上的泛化能力，实验结果表明本方法在 PACS、VLCS、Office-Home 和 TerraIncognita 等数据集上表现优异。