In Domain Adaptation (DA), where the feature distributions of the source and target domains are different, various distance-based methods have been proposed to minimize the discrepancy between the source and target domains to handle the domain shift. In this paper, we propose a new similarity function, which is called Population Correlation (PC), to measure the domain discrepancy for DA. Base on the PC function, we propose a new method called Domain Adaptation by Maximizing Population Correlation (DAMPC) to learn a domain-invariant feature representation for DA. Moreover, most existing DA methods use hand-crafted bottleneck networks, which may limit the capacity and flexibility of the corresponding model. Therefore, we further propose a method called DAMPC with Neural Architecture Search (DAMPC-NAS) to search the optimal network architecture for DAMPC. Experiments on several benchmark datasets, including Office-31, Office-Home, and VisDA-2017, show that the proposed DAMPC-NAS method achieves better results than state-of-the-art DA methods.
翻译:在域适应(DA)中,源和目标域的特征分布不同,因此提出了各种基于远程的方法,以尽量减少源和目标域在处理域变换方面的差异;在本文件中,我们提出了一个新的类似功能,称为人口关联(PC),以衡量DA的域差异;在PC函数的基础上,我们提出了一种名为“域适应”的新方法,即通过最大限度地增加人口关联(DAMPC)来学习DA的域-异差特征。此外,大多数现有的DA方法使用手工制造的瓶颈网络,这可能会限制相应模型的容量和灵活性。因此,我们进一步提出了称为DAMPC的神经结构搜索(DAMPC-NAS)的方法,以寻找DAMPC的最佳网络架构。在几个基准数据集(包括办公室31、办公室Home和VisDA-2017)上进行的实验表明,拟议的DAMPC-NAS方法取得了比州一级DA方法更好的结果。