Domain adaptation (DA) aims at facilitating the target model training by leveraging knowledge from related but distribution-inconsistent source domain. Most of the previous DA works concentrate on homogeneous scenarios, where the source and target domains are assumed to share the same feature space. Nevertheless, frequently, in reality, the domains are not consistent in not only data distribution but also the representation space and feature dimensions. That is, these domains are heterogeneous. Although many works have attempted to handle such heterogeneous DA (HDA) by transforming HDA to homogeneous counterparts or performing DA jointly with domain transformation, nearly all of them just concentrate on the feature and distribution alignment across domains, neglecting the structure and classification space preservation for domains themselves. In this work, we propose a novel HDA model, namely, heterogeneous classification space alignment (HCSA), which leverages knowledge from both the source samples and model parameters to the target. In HCSA, structure preservation, distribution, and classification space alignment are implemented, jointly with feature representation by transferring both the source-domain representation and model knowledge. Moreover, we design an alternating algorithm to optimize the HCSA model with guaranteed convergence and complexity analysis. In addition, the HCSA model is further extended with deep network architecture. Finally, we experimentally evaluate the effectiveness of the proposed method by showing its superiority to the compared approaches.

中文翻译：

---

具有结构和分类空间对齐的异构域适应


领域适应（DA）旨在通过利用相关但分布不一致的源领域的知识来促进目标模型训练。之前的大多数 DA 工作都集中在同质场景上，其中源域和目标域被假设共享相同的特征空间。然而，实际上，这些域不仅在数据分布上而且在表示空间和特征维度上也不一致。也就是说，这些域是异构的。尽管许多工作尝试通过将 HDA 转换为同质对应物或与域变换联合执行 DA 来处理这种异构 DA (HDA)，但几乎所有的工作都只关注跨域的特征和分布对齐，忽略了结构和分类空间保存。域本身。在这项工作中，我们提出了一种新颖的 HDA 模型，即异构分类空间对齐（HCSA），它利用来自源样本和模型参数的知识到目标。在 HCSA 中，通过传输源域表示和模型知识，与特征表示一起实现结构保存、分布和分类空间对齐。此外，我们设计了一种交替算法来优化 HCSA 模型，并保证收敛性和复杂性分析。此外，HCSA模型通过深层网络架构进一步扩展。最后，我们通过实验评估所提出方法的有效性，显示其相对于比较方法的优越性。