Domain adaptation (DA) aims to train a robust predictor by transferring rich knowledge from a well-labeled source domain to annotate a newly coming target domain; however, the two domains are usually drawn from very different distributions. Most current methods either learn the common features by matching inter-domain feature distributions and training the classifier separately or align inter-domain label distributions to directly obtain an adaptive classifier based on the original features despite feature distortion. Moreover, intra-domain information may be greatly degraded during the DA process; i.e., the source data samples from different classes might grow closer. To this end, this paper proposes a novel DA approach, referred to as inter-class distribution alienation and inter-domain distribution alignment based on manifold embedding (IDAME). Specifically, IDAME commits to adapting the classifier on the Grassmann manifold by using structural risk minimization, where inter-domain feature distributions are aligned to mitigate feature distortion, and the target pseudo labels are exploited using the distances on the Grassmann manifold. During the classifier adaptation process, we simultaneously consider the inter-class distribution alienation, the inter-domain distribution alignment, and the manifold consistency. Extensive experiments validate that IDAME can outperform several comparative state-of-the-art methods on real-world cross-domain image datasets.

中文翻译：

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基于流形嵌入的类间分布异化和域间分布对齐

领域适应（DA）的目的是通过从标记良好的源域转移丰富的知识，以注释新出现的目标域，从而训练鲁棒的预测器；但是，这两个域通常来自非常不同的分布。当前大多数方法要么通过匹配域间特征分布并分别训练分类器来学习共同特征，要么对齐域间标签分布以基于原始特征直接获得自适应分类器，尽管特征失真。此外，在DA处理过程中，域内信息可能会大大降低；也就是说，来自不同类别的源数据样本可能会越来越近。为此，本文提出了一种新颖的DA方法，即基于流形嵌入（IDAME）的类间分布异化和域间分布对齐。具体而言，IDAME致力于通过使用结构风险最小化来适应Grassmann流形上的分类器，其中将域间特征分布对齐以减轻特征失真，并使用Grassmann流形上的距离来利用目标伪标记。在分类器适应过程中，我们同时考虑了类间分布异化，域间分布对齐和流形一致性。大量实验证明，IDAME可以胜过现实世界中跨域图像数据集上几种比较先进的方法。使用伪格拉斯曼流形上的距离利用目标伪标记。在分类器适应过程中，我们同时考虑了类间分布异化，域间分布对齐和流形一致性。大量实验证明，IDAME可以胜过现实世界中跨域图像数据集上几种比较先进的方法。使用伪格拉斯曼流形上的距离利用目标伪标记。在分类器适应过程中，我们同时考虑了类间分布异化，域间分布对齐和流形一致性。大量实验证明，IDAME可以胜过现实世界中跨域图像数据集上几种比较先进的方法。