  In the classic setting of unsupervised domain adaptation (UDA), the labeled
source data are available in the training phase. However, in many real-world
scenarios, owing to some reasons such as privacy protection and information
security, the source data is inaccessible, and only a model trained on the
source domain is available. This paper proposes a novel deep clustering method
for this challenging task. Aiming at the dynamical clustering at feature-level,
we introduce extra constraints hidden in the geometric structure between data
to assist the process. Concretely, we propose a geometry-based constraint,
named semantic consistency on the nearest neighborhood (SCNNH), and use it to
encourage robust clustering. To reach this goal, we construct the nearest
neighborhood for every target data and take it as the fundamental clustering
unit by building our objective on the geometry. Also, we develop a more
SCNNH-compliant structure with an additional semantic credibility constraint,
named semantic hyper-nearest neighborhood (SHNNH). After that, we extend our
method to this new geometry. Extensive experiments on three challenging UDA
datasets indicate that our method achieves state-of-the-art results. The
proposed method has significant improvement on all datasets (as we adopt SHNNH,
the average accuracy increases by over 3.0% on the large-scaled dataset). Code
is available at https://github.com/tntek/N2DCX.
