  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.
