  Unsupervised Domain adaptation is an effective method in addressing the
domain shift issue when transferring knowledge from an existing richly labeled
domain to a new domain. Existing manifold-based methods either are based on
traditional models or largely rely on Grassmannian manifold via minimizing
differences of single covariance matrices of two domains. In addition, existing
pseudo-labeling algorithms inadequately consider the quality of pseudo labels
in aligning the conditional distribution between two domains. In this work, a
deep spherical manifold Gaussian kernel (DSGK) framework is proposed to map the
source and target subspaces into a spherical manifold and reduce the
discrepancy between them by embedding both extracted features and a Gaussian
kernel. To align the conditional distributions, we further develop an
easy-to-hard pseudo label refinement process to improve the quality of the
pseudo labels and then reduce categorical spherical manifold Gaussian kernel
geodesic loss. Extensive experimental results show that DSGK outperforms
state-of-the-art methods, especially on challenging cross-domain learning
tasks.
