Continual domain shift poses a significant challenge in real-world
applications, particularly in situations where labeled data is not available
for new domains. The challenge of acquiring knowledge in this problem setting
is referred to as unsupervised continual domain shift learning. Existing
methods for domain adaptation and generalization have limitations in addressing
this issue, as they focus either on adapting to a specific domain or
generalizing to unseen domains, but not both. In this paper, we propose
Complementary Domain Adaptation and Generalization (CoDAG), a simple yet
effective learning framework that combines domain adaptation and generalization
in a complementary manner to achieve three major goals of unsupervised
continual domain shift learning: adapting to a current domain, generalizing to
unseen domains, and preventing forgetting of previously seen domains. Our
approach is model-agnostic, meaning that it is compatible with any existing
domain adaptation and generalization algorithms. We evaluate CoDAG on several
benchmark datasets and demonstrate that our model outperforms state-of-the-art
models in all datasets and evaluation metrics, highlighting its effectiveness
and robustness in handling unsupervised continual domain shift learning.

本文提出了一种模型无关的、能够将适应和泛化结合起来的学习方法 CoDAG，主要解决无监督连续域漂移学习的三个问题，即适应当前域、泛化到看不见的域，以及防止遗忘先前看到的域，并在多个基准数据集上展示了其有效性和鲁棒性。