Adversarial training is a useful approach to promote the learning of
transferable representations across the source and target domains, which has
been widely applied for domain adaptation (DA) tasks based on deep neural
networks. Until very recently, existing adversarial domain adaptation (ADA)
methods ignore the useful information from the label space, which is an
important factor accountable for the complicated data distributions associated
with different semantic classes. Especially, the inter-class semantic
relationships have been rarely considered and discussed in the current work of
transfer learning. In this paper, we propose a novel relationship-aware
adversarial domain adaptation (RADA) algorithm, which first utilizes a single
multi-class domain discriminator to enforce the learning of inter-class
dependency structure during domain-adversarial training and then aligns this
structure with the inter-class dependencies that are characterized from
training the label predictor on source domain. Specifically, we impose a
regularization term to penalize the structure discrepancy between the
inter-class dependencies respectively estimated from domain discriminator and
label predictor. Through this alignment, our proposed method makes the
adversarial domain adaptation aware of the class relationships. Empirical
studies show that the incorporation of class relationships significantly
improves the performance on benchmark datasets.

本文提出了一种新的关系感知对抗域自适应（RADA）算法，通过使用一个单一的多类域鉴别器，强制进行领域对抗训练的同时学习 inter-class 依赖关系结构，并将此结构与从源域的标签预测器中表征出的 inter-class 依赖关系对齐，证明了该方法在改善基准数据集性能方面显著提高了类关系的应用。