  We propose a new general Graph Adversarial Domain Adaptation (GADA) based on
semantic knowledge reasoning of class structure for solving the problem of
unsupervised domain adaptation (UDA) from the big data with non-shared and
imbalanced classes to specified small and imbalanced applications (NI-UDA),
where non-shared classes mean the label space out of the target domain. Our
goal is to leverage priori hierarchy knowledge to enhance domain adversarial
aligned feature representation with graph reasoning. In this paper, to address
two challenges in NI-UDA, we equip adversarial domain adaptation with Hierarchy
Graph Reasoning (HGR) layer and the Source Classifier Filter (SCF). For sparse
classes transfer challenge, our HGR layer can aggregate local feature to
hierarchy graph nodes by node prediction and enhance domain adversarial aligned
feature with hierarchy graph reasoning for sparse classes. Our HGR contributes
to learn direct semantic patterns for sparse classes by hierarchy attention in
self-attention, non-linear mapping and graph normalization. our SCF is proposed
for the challenge of knowledge sharing from non-shared data without negative
transfer effect by filtering low-confidence non-shared data in HGR layer.
Experiments on two benchmark datasets show our GADA methods consistently
improve the state-of-the-art adversarial UDA algorithms, e.g. GADA(HGR) can
greatly improve f1 of the MDD by \textbf{7.19\%} and GVB-GD by \textbf{7.89\%}
respectively on imbalanced source task in Meal300 dataset. The code is
available at https://gadatransfer.wixsite.com/gada.
