The generalization capability of machine learning models, which refers to
generalizing the knowledge for an "unseen" domain via learning from one or
multiple seen domain(s), is of great importance to develop and deploy machine
learning applications in the real-world conditions. Domain Generalization (DG)
techniques aim to enhance such generalization capability of machine learning
models, where the learnt feature representation and the classifier are two
crucial factors to improve generalization and make decisions. In this paper, we
propose Discriminative Adversarial Domain Generalization (DADG) with
meta-learning-based cross-domain validation. Our proposed framework contains
two main components that work synergistically to build a domain-generalized DNN
model: (i) discriminative adversarial learning, which proactively learns a
generalized feature representation on multiple "seen" domains, and (ii)
meta-learning based cross-domain validation, which simulates train/test domain
shift via applying meta-learning techniques in the training process. In the
experimental evaluation, a comprehensive comparison has been made among our
proposed approach and other existing approaches on three benchmark datasets.
The results shown that DADG consistently outperforms a strong baseline DeepAll,
and outperforms the other existing DG algorithms in most of the evaluation
cases.

本文提出基于元学习的 Discriminative Adversarial Domain Generalization（DADG）框架来改善机器学习模型的泛化能力，包括学习一般化的特征表示和分类器，在三个基准数据集的对比中，DADG 始终优于基线 DeepAll，并在大多数评估情况下优于其他现有的领域泛化算法。