互补域适应与泛化用于无监督持续域偏移学习
Complementary Domain Adaptation and Generalization for Unsupervised Continual Domain Shift Learning
AI总结：
针对无监督持续域偏移学习，提出互补域适应与泛化框架CoDAG，兼顾适应当前域、泛化未见域与防遗忘，模型无关且在多个基准上超越现有方法。
摘要：
持续域偏移在现实应用中构成了重大挑战，尤其是在新域没有标记数据的情况下。在此问题设置中获取知识的挑战被称为无监督持续域偏移学习。现有的域适应和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，要么专注于泛化到未见域，但不能同时兼顾两者。在本文中，我们提出了互补域适应与泛化（CoDAG），这是一个简单而有效的学习框架，以互补的方式结合了域适应和泛化，以实现无监督持续域偏移学习的三个主要目标：适应当前域、泛化到未见域以及防止遗忘先前见过的域。我们的方法是模型无关的，这意味着它与任何现有的域适应和泛化算法兼容。我们在多个基准数据集上评估了CoDAG，并证明我们的模型在所有数据集和评估指标上均优于最先进的模型，突显了其在处理无监督持续域偏移学习方面的有效性和鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
