Domain Generalization Via Adversarially Learned Novel Domains

This paper focuses on the domain generalization task, which aims to learn a model that generalizes to unseen domains by utilizing multiple training domains. More specifically, we follow the idea of adversarial data augmentation, which aims to synthesize and augment training data with “hard” domains for improving the model's domain generalization ability. Previous works augment training data only with samples similar to the training data, resulting in limited generalization ability. We propose a novel adversarial data augmentation method, termed GADA (Generative Adversarial Domain Augmentation), which employs an image-to-image translation model to obtain a distribution of novel domains that are semantically different from the training domains, and, at the same time, hard to classify. Evaluation and further analysis suggest that adversarial data augmentation with semantically different samples leads to better domain generalization performance.