Geometric Knowledge Embedding for unsupervised domain adaptation

Hanrui Wu, Yuguang Yan, Yuzhong Ye, Michael K. Ng, Qingyao Wu

Abstract: Highlights•We exploit the geometric information of the source and target data to learn discriminative representations. In this sense, the features of each sample and its neighbors are both considered during the learning procedure.•We introduce MMD into the graph convolutional network to explore geometric knowledge for learning transferable embeddings.•GCN has not been applied to domain adaptation problems before, and this makes our proposed method a decent supplement to existing domain adaptation approaches.•We conduct comprehensive experiments on four real-world applications, including object recognition, image classification and text categorization, to demonstrate the effectiveness of our proposed method.