论文标题
多步在线无监督的域名适应
Multi-step Online Unsupervised Domain Adaptation
论文作者
论文摘要
在本文中，我们解决了在线无监督的域适应性（OUDA）问题，其中目标数据未标记并依次到达。关于OUDA问题的传统方法主要集中于将每个到达目标数据转换为源域，并且它们没有足够考虑到达目标数据之间的时间相干性和累积统计。我们为OUDA问题提出了一个多步框架，该框架提出了一种新型方法，以计算以欧几里得空间上几何解释启发的平均目标子空间。这个平均目标子空间包含到达目标数据中的累积时间信息。此外，从均值目标子空间计算得出的转换矩阵被应用于下一个目标数据，作为预处理步骤，将目标数据与源域更接近对齐。四个数据集上的实验证明了我们提出的多步ouda框架中的每个步骤及其在先前方法上的性能。
In this paper, we address the Online Unsupervised Domain Adaptation (OUDA) problem, where the target data are unlabelled and arriving sequentially. The traditional methods on the OUDA problem mainly focus on transforming each arriving target data to the source domain, and they do not sufficiently consider the temporal coherency and accumulative statistics among the arriving target data. We propose a multi-step framework for the OUDA problem, which institutes a novel method to compute the mean-target subspace inspired by the geometrical interpretation on the Euclidean space. This mean-target subspace contains accumulative temporal information among the arrived target data. Moreover, the transformation matrix computed from the mean-target subspace is applied to the next target data as a preprocessing step, aligning the target data closer to the source domain. Experiments on four datasets demonstrated the contribution of each step in our proposed multi-step OUDA framework and its performance over previous approaches.
