CNN Feature Map Augmentation for Single-Source Domain Generalization
This addresses the issue of deep learning models failing to generalize to unseen data domains, which is critical for real-world applications, though it appears incremental as it builds on existing regularization methods.
The paper tackled the problem of domain generalization in image classification by proposing a novel feature map augmentation technique for CNNs, which improved cross-domain performance and surpassed state-of-the-art methods on benchmark datasets like PACS, VLCS, Office-Home, and TerraIncognita.
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.