Abstract Heterogeneous domain adaptation (HDA) aims to leverage knowledge from a source domain for helping learn an accurate model in a heterogeneous target domain. HDA is exceedingly challenging since the feature spaces of domains are distinct. To tackle this issue, we propose a unified learning framework called Discriminative Distribution Alignment (DDA) for deriving a domain-invariant subspace. The proposed DDA can simultaneously match the discriminative directions of domains, align the distributions across domains, and enhance the separability of data during adaptation. To achieve this, DDA trains an adaptive classifier by both reducing the distribution divergence and enlarging distances between class centroids. Based on the proposed DDA framework, we further develop two methods, by embedding the cross-entropy loss and squared loss into this framework, respectively. We conduct experiments on the tasks of categorization across domains and modalities. Experimental results clearly demonstrate that the proposed DDA outperforms several state-of-the-art models.

中文翻译：

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判别分布对齐：异构域适应的统一框架

摘要 异构域自适应 (HDA) 旨在利用源域中的知识帮助学习异构目标域中的准确模型。HDA 极具挑战性，因为域的特征空间是不同的。为了解决这个问题，我们提出了一个统一的学习框架，称为判别分布对齐（DDA），用于推导域不变子空间。所提出的 DDA 可以同时匹配域的判别方向，对齐跨域的分布，并在适应过程中增强数据的可分离性。为了实现这一点，DDA 通过减少分布散度和扩大类质心之间的距离来训练自适应分类器。基于提出的 DDA 框架，我们进一步开发了两种方法，通过将交叉熵损失和平方损失分别嵌入到这个框架中。我们对跨领域和模式的分类任务进行了实验。实验结果清楚地表明，所提出的 DDA 优于几种最先进的模型。