Heterogeneous unsupervised domain adaptation (HUDA) is the most challenging domain adaptation setting where the feature space differs between source and target domains, and the target domain has only unlabeled data. Existing HUDA methods assume that both positive and negative examples are available in the source domain, which may not be satisfied in some real applications. This paper addresses a new challenging setting called positive and unlabeled heterogeneous domain adaptation (PU-HDA), a HUDA setting where the source domain only has positives. PU-HDA can also be viewed as an extension of PU learning where the positive and unlabeled examples are sampled from different domains. A naive combination of existing HUDA and PU learning methods is ineffective in PU-HDA due to the gap in label distribution between the source and target domains. To overcome this issue, we propose a novel method, positive-adversarial domain adaptation (PADA), which can predict likely positive examples from the unlabeled target data and simultaneously align the feature spaces to reduce the distribution divergence between the whole source data and the likely positive target data. PADA achieves this by a unified adversarial training framework for learning a classifier to predict positive examples and a feature transformer to transform the target feature space to that of the source. Specifically, they are both trained to fool a common discriminator that determines whether the likely positive examples are from the target or source domain. We experimentally show that PADA outperforms several baseline methods, such as the naive combination of HUDA and PU learning.

翻译：摘要：异构无监督域自适应（HUDA）是最具挑战性的域自适应设置，其中源域和目标域的特征空间不同，且目标域仅包含无标签数据。现有HUDA方法假设源域中同时存在正类和负类样本，但在某些实际应用中该条件可能无法满足。本文提出了一种称为正类与无标签异构域自适应（PU-HDA）的新型挑战性设置，该设置下源域仅包含正类样本。PU-HDA可视为PU学习的扩展，其中正类和无标签样本来自不同域。由于源域与目标域之间的标签分布差异，现有HUDA与PU学习方法的简单组合在PU-HDA中效果不佳。为解决此问题，我们提出了一种新方法——正类对抗域自适应（PADA），该方法能从无标签目标数据中预测可能正类样本，同时对齐特征空间以减小整个源数据与可能正类目标数据之间的分布差异。PADA通过统一对抗训练框架实现：学习一个分类器用于预测正类样本，同时训练一个特征变换器将目标特征空间映射至源特征空间。具体而言，两者均被训练以欺骗一个共同的判别器，该判别器用于判断可能正类样本来自目标域还是源域。实验表明，PADA在多种基线方法（如HUDA与PU学习的简单组合）上均表现更优。