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.

翻译：连续域漂移在实际应用中构成重大挑战，尤其在标注数据无法获取的新领域场景下。这种问题设定中的知识获取挑战被称为无监督连续域漂移学习。现有域适应与泛化方法在解决该问题上存在局限性，因为它们要么专注于适应特定领域，要么专注于泛化到未见领域，无法同时兼顾两者。本文提出互补域适应与泛化（CoDAG）——一种简单而有效的学习框架，通过互补方式结合域适应与泛化，以实现无监督连续域漂移学习的三大核心目标：适应当前领域、泛化至未见领域、以及防止遗忘已见领域。我们的方法具有模型无关性，即能与任意现有域适应与泛化算法兼容。在多个基准数据集上的评估表明，CoDAG在所有数据集和评价指标上均 outperforms 当前最优模型，凸显了其在处理无监督连续域漂移学习中的有效性与鲁棒性。