Cross-Domain Graph Convolutions for Adversarial Unsupervised Domain Adaptation
Ronghang Zhu, Xiaodong Jiang, Jiasen Lu, Sheng Li
Abstract
Unsupervised domain adaptation (UDA) has attracted increasing attention in recent years, which adapts classifiers to an unlabeled target domain by exploiting a labeled source domain. To reduce the discrepancy between source and target domains, adversarial learning methods are typically selected to seek domain-invariant representations by confusing the domain discriminator. However, classifiers may not be well adapted to such a domain-invariant representation space, as the sample- and class-level data structures could be distorted during adversarial learning. In this article, we propose a novel transferable feature learning approach on graphs (TFLG) for unsupervised adversarial domain adaptation (DA), which jointly incorporates sample- and class-level structure information across two domains. TFLG first constructs graphs for minibatch samples and identifies the classwise correspondence across domains. A novel cross-domain graph convolutional operation is designed to jointly align the sample- and class-level structures in two domains. Moreover, a memory bank is designed to further exploit the class-level information. Extensive experiments on benchmark datasets demonstrate the effectiveness of our approach compared to the state-of-the-art UDA methods.