论文标题
域的适应性与可因素的关节移位
Domain Adaptation with Factorizable Joint Shift
论文作者
论文摘要
现有的域适应性（DA）通常假设域移位来自协变量或标签。但是，在实际应用中，从不同域中选择的样品在协变量和标签中都可能具有偏差。在本文中，我们提出了一个新的假设，即可分解的关节移位（FJ），以处理协变量和标签中采样偏差的共存。尽管允许双方转移，但FJ假定这两个因素之间的偏见是独立性的。我们对FJ何时退化为先前的假设以及必要时提供了理论和经验理解。我们进一步提出了联合重要性一致性（JIA），这是一个歧视性学习目标，旨在为受监督和无监督的领域适应性获得联合重要性估计量。我们的方法可以无缝地与现有的域适应算法合并，以更好地对训练数据进行更重要的估计和加权。合成数据集的实验证明了我们方法的优势。
Existing domain adaptation (DA) usually assumes the domain shift comes from either the covariates or the labels. However, in real-world applications, samples selected from different domains could have biases in both the covariates and the labels. In this paper, we propose a new assumption, Factorizable Joint Shift (FJS), to handle the co-existence of sampling bias in covariates and labels. Although allowing for the shift from both sides, FJS assumes the independence of the bias between the two factors. We provide theoretical and empirical understandings about when FJS degenerates to prior assumptions and when it is necessary. We further propose Joint Importance Aligning (JIA), a discriminative learning objective to obtain joint importance estimators for both supervised and unsupervised domain adaptation. Our method can be seamlessly incorporated with existing domain adaptation algorithms for better importance estimation and weighting on the training data. Experiments on a synthetic dataset demonstrate the advantage of our method.
