Domain adaptation aims to mitigate the domain shift problem when transferring knowledge from one domain into another similar but different domain. However, most existing works rely on extracting marginal features without considering class labels. Moreover, some methods name their model as so-called unsupervised domain adaptation while tuning the parameters using the target domain label. To address these issues, we propose a novel approach called correlated adversarial joint discrepancy adaptation network (CAJNet), which minimizes the joint discrepancy of two domains and achieves competitive performance with tuning parameters using the correlated label. By training the joint features, we can align the marginal and conditional distributions between the two domains. In addition, we introduce a probability-based top-$\mathcal{K}$ correlated label ($\mathcal{K}$-label), which is a powerful indicator of the target domain and effective metric to tune parameters to aid predictions. Extensive experiments on benchmark datasets demonstrate significant improvements in classification accuracy over the state of the art.

翻译：在将知识从一个领域转移到另一个类似但不同的领域时,对域的适应旨在缓解领域转移问题。然而,大多数现有工作依靠提取边际特征,而不考虑类标签。此外,有些方法将模型命名为所谓的不受监督的域适应,同时调整使用目标域标签的参数。为了解决这些问题,我们提议了一种新颖的方法,称为相对对称的对立联合差异适应网络(CAJNet),它最大限度地减少两个领域之间的联合差异,并用相关标签调试参数实现竞争性性能。通过训练联合功能,我们可以将两个区域之间的边际和有条件分布相匹配。此外,我们引入一个基于概率的顶值-$\ mathcal{K}$的关联标签($\mathcal{K}-label),这是目标域的有力指标,也是调和参数以帮助预测的有效衡量标准。基准数据集的广泛实验显示,在分类精确度方面比艺术状态有显著改进。