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.

翻译：无监督领域自适应（UDA）是机器学习中将域内模型扩展到数据分布存在差异的目标领域的关键形式。以往研究多聚焦于捕获域间可迁移性，但很大程度上忽视了丰富的域内结构，经验上导致判别性进一步下降。为此，我们提出一种全新的图谱对齐（SPA）框架来平衡这一权衡。该方法的核心简要概括如下：（i）通过将领域自适应问题映射为图基元，SPA构建了一种粗粒度图对齐机制，并引入新颖的谱正则化器以实现域图在特征空间上的对齐；（ii）我们进一步开发了基于邻域感知自训练机制的细粒度消息传播模块，以增强目标域的判别性。在标准化基准测试中，SPA的全面实验表明其性能已超越现有最先进的领域自适应方法。结合密集的模型分析，我们得出结论：该方法确实具有优越的有效性、鲁棒性、判别性和可迁移性。代码与数据可通过以下链接获取：https://github.com/CrownX/SPA。