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.

本文提出了判别性径向领域适应方法（DRDA），通过共享径向结构来缩小源域和目标域之间的差异，以增强特征的传递性和可区分性。在多个基准测试中进行了广泛实验，表明该方法在各种任务上均优于现有方法，包括典型的无监督域适应，多源域适应，域不可知学习和域泛化。