Unsupervised domain adaptation (UDA) is a pivotal form in machine learning to extend the in-domain model to the distinctive target domains where the data distributions differ. Most prior works focus on capturing the inter-domain transferability but largely overlook rich intra-domain structures, which empirically results in even worse discriminability. In this work, we introduce a novel graph SPectral Alignment (SPA) framework to tackle the tradeoff. The core of our method is briefly condensed as follows: (i)-by casting the DA problem to graph primitives, SPA composes a coarse graph alignment mechanism with a novel spectral regularizer towards aligning the domain graphs in eigenspaces; (ii)-we further develop a fine-grained message propagation module -- upon a novel neighbor-aware self-training mechanism -- in order for enhanced discriminability in the target domain. On standardized benchmarks, the extensive experiments of SPA demonstrate that its performance has surpassed the existing cutting-edge DA methods. Coupled with dense model analysis, we conclude that our approach indeed possesses superior efficacy, robustness, discriminability, and transferability. Code and data are available at: https://github.com/CrownX/SPA.

我们介绍了一种新颖的图谱对齐框架(SPA)，用于解决无监督领域适应问题，该框架通过将DA问题投射到图基元上，通过一种新颖的谱正则化器在特定的特征空间中对领域图进行对齐，进而通过新颖的邻域感知自训练机制开发细粒度的消息传递模块，以提高目标领域中的可区分度。在标准基准测试中，SPA的广泛实验证明其性能已超越现有的领域适应方法，密集模型分析证实了我们的方法确实具有优越的效果、鲁棒性、可区分度和可迁移性。

SPA：一种基于图谱对齐的领域自适应视角