  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.
