  Open-set domain adaptation (OSDA) considers that the target domain contains
samples from novel categories unobserved in external source domain.
Unfortunately, existing OSDA methods always ignore the demand for the
information of unseen categories and simply recognize them as "unknown" set
without further explanation. This motivates us to understand the unknown
categories more specifically by exploring the underlying structures and
recovering their interpretable semantic attributes. In this paper, we propose a
novel framework to accurately identify the seen categories in target domain,
and effectively recover the semantic attributes for unseen categories.
Specifically, structure preserving partial alignment is developed to recognize
the seen categories through domain-invariant feature learning. Attribute
propagation over visual graph is designed to smoothly transit attributes from
seen to unseen categories via visual-semantic mapping. Moreover, two new
cross-main benchmarks are constructed to evaluate the proposed framework in the
novel and practical challenge. Experimental results on open-set recognition and
semantic recovery demonstrate the superiority of the proposed method over other
compared baselines.
