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：持续域偏移在现实应用中构成显著挑战，尤其在目标域缺乏标注数据的情况下。该问题设定下的知识获取被称为无监督持续域偏移学习。现有域适应与泛化方法在解决此问题方面存在局限性，因为它们要么专注于适应特定域，要么专注于泛化至未见域，但无法同时兼顾两者。本文提出互补域适应与泛化（CoDAG）——一个简洁高效的学习框架，通过互补方式融合域适应与泛化，以实现无监督持续域偏移学习的三大核心目标：适应当前域、泛化至未见域、以及防止对先前已见域的遗忘。本方法具有模型无关性，即与任何现有域适应与泛化算法均兼容。我们在多个基准数据集上评估CoDAG，结果显示该模型在所有数据集与评估指标上均超越现有最优模型，充分彰显其处理无监督持续域偏移学习的有效性与鲁棒性。