Domain adaptation methods reduce domain shift typically by learning domain-invariant features. Most existing methods are built on distribution matching, e.g., adversarial domain adaptation, which tends to corrupt feature discriminability. In this paper, we propose Discriminative Radial Domain Adaptation (DRDA) which bridges source and target domains via a shared radial structure. It's motivated by the observation that as the model is trained to be progressively discriminative, features of different categories expand outwards in different directions, forming a radial structure. We show that transferring such an inherently discriminative structure would enable to enhance feature transferability and discriminability simultaneously. Specifically, we represent each domain with a global anchor and each category a local anchor to form a radial structure and reduce domain shift via structure matching. It consists of two parts, namely isometric transformation to align the structure globally and local refinement to match each category. To enhance the discriminability of the structure, we further encourage samples to cluster close to the corresponding local anchors based on optimal-transport assignment. Extensively experimenting on multiple benchmarks, our method is shown to consistently outperforms state-of-the-art approaches on varied tasks, including the typical unsupervised domain adaptation, multi-source domain adaptation, domain-agnostic learning, and domain generalization.

翻译：领域自适应方法通常通过学习域不变特征来减少领域偏移。现有方法大多基于分布匹配（如对抗性领域自适应），但这容易破坏特征判别性。本文提出判别式径向领域自适应（DRRA），通过共享径向结构桥接源域与目标域。其动机源于以下观察：随着模型逐步训练至具备判别性，不同类别的特征沿不同方向向外扩展，形成径向结构。研究表明，迁移这种内在判别性结构可同时增强特征的可迁移性与判别性。具体而言，我们为每个域构建全局锚点，为每个类别构建局部锚点以形成径向结构，并通过结构匹配减少领域偏移。该方法包含两部分：等距变换实现全局结构对齐，以及局部细化实现类别级匹配。为增强结构的判别性，我们进一步基于最优传输分配方法，促使样本聚集至对应局部锚点附近。在多个基准上的大量实验表明，本方法在各类任务（包括典型无监督领域自适应、多源领域自适应、域无关学习及域泛化）中均持续优于现有最优方法。