Most existing domain adaptation methods assume that the label space of the source domain is the same as the label space of the target domain. However, this assumption is generally untenable due to the differences between the two domains. Therefore, a novel domain adaptation paradigm called Partial Domain Adaptation (PDA), which only assumes that the source label space is large enough to subsume the target label space has been proposed recently to relax such strict assumption. Previous partial domain adaptation methods mainly utilize weighting mechanisms to alleviate negative transfer caused by outlier classes samples. Though these methods have achieved high performance in PDA tasks, all the heterogeneous data is retained during the whole training process, which still contributes to negative transfer. In this work, we propose a shared class oriented adversarial network (SCOAN) for partial domain adaptation. Outlier samples are excluded from training process via weighting strategy to entirely circumvent negative transfer and positive transfer is performed by combining adversarial network and Maximum Mean Discrepancy (MMD) to bridge domain gap. Multi-classifier module is proposed to further improve the generalization ability of the network. Extensive experiments show that SCOAN achieves state-of-the-art results on several benchmark partial domain adaptation datasets.

现有的大多数域适配方法都假定源域的标签空间与目标域的标签空间相同。但是，由于两个域之间的差异，这种假设通常是站不住脚的。因此，最近提出了一种新颖的域自适应范式，称为部分域自适应（PDA），它仅假定源标签空间足够大以包含目标标签空间，以放松这种严格的假设。先前的部分域自适应方法主要利用加权机制来减轻由异常类样本引起的负转移。尽管这些方法已在PDA任务中实现了高性能，但所有异类数据在整个训练过程中均得以保留，这仍然会导致负向传递。在这项工作中 我们提出了一种面向部分领域的共享的面向类的对抗网络（SCOAN）。通过加权策略将异常值样本从训练过程中排除，以完全避免负迁移，并通过将对抗性网络和最大均值误差（MMD）结合起来以弥合域差距来执行正迁移。提出了多分类器模块，以进一步提高网络的泛化能力。大量的实验表明，SCOAN在几个基准部分域适应数据集上取得了最先进的结果。提出了多分类器模块，以进一步提高网络的泛化能力。大量的实验表明，SCOAN在几个基准的部分域适应数据集上取得了最先进的结果。提出了多分类器模块，以进一步提高网络的泛化能力。大量的实验表明，SCOAN在几个基准部分域适应数据集上取得了最先进的结果。