Heterogeneous domain adaptation (HDA) tackles the learning of cross-domain samples with both different probability distributions and feature representations. Most of the existing HDA studies focus on the single-source scenario. In reality, however, it is not uncommon to obtain samples from multiple heterogeneous domains. In this article, we study the multisource HDA problem and propose a conditional weighting adversarial network (CWAN) to address it. The proposed CWAN adversarially learns a feature transformer, a label classifier, and a domain discriminator. To quantify the importance of different source domains, CWAN introduces a sophisticated conditional weighting scheme to calculate the weights of the source domains according to the conditional distribution divergence between the source and target domains. Different from existing weighting schemes, the proposed conditional weighting scheme not only weights the source domains but also implicitly aligns the conditional distributions during the optimization process. Experimental results clearly demonstrate that the proposed CWAN performs much better than several state-of-the-art methods on four real-world datasets.
翻译:不同域适应(HDA)处理跨域样本的学习,其概率分布和特征表现各不相同。现有的HDA研究大多侧重于单一源情景。但实际上,从多种不同域获取样本并不罕见。在本条中,我们研究多种源的HDA问题,并提出一个有条件加权对称的对抗网络(CWAN)来解决该问题。拟议的CWAN对抗性学习一个地貌变异器、一个标签分类器和一个域区分器。为了量化不同源域的重要性,CWAN采用了一个复杂的有条件加权办法,根据源和目标域之间的有条件分布差异计算源域的权重。与现有的加权办法不同,拟议的有条件加权办法不仅加权了源域,而且隐含地调整了优化过程中的有条件分布。实验结果清楚地表明,拟议的CWAN在四个现实世界数据集上比几个最先进的方法要好得多。