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.
Cross domain adaptation by learning partially shared classifiers and weighting source data points in the shared subspaces