  Domain adaptation aims to leverage a label-rich domain (the source domain) to
help model learning in a label-scarce domain (the target domain). Most domain
adaptation methods require the co-existence of source and target domain samples
to reduce the distribution mismatch, however, access to the source domain
samples may not always be feasible in the real world applications due to
different problems (e.g., storage, transmission, and privacy issues). In this
work, we deal with the source data-free unsupervised domain adaptation problem,
and propose a novel approach referred to as Virtual Domain Modeling (VDM-DA).
The virtual domain acts as a bridge between the source and target domains. On
one hand, we generate virtual domain samples based on an approximated Gaussian
Mixture Model (GMM) in the feature space with the pre-trained source model,
such that the virtual domain maintains a similar distribution with the source
domain without accessing to the original source data. On the other hand, we
also design an effective distribution alignment method to reduce the
distribution divergence between the virtual domain and the target domain by
gradually improving the compactness of the target domain distribution through
model learning. In this way, we successfully achieve the goal of distribution
alignment between the source and target domains by training deep networks
without accessing to the source domain data. We conduct extensive experiments
on benchmark datasets for both 2D image-based and 3D point cloud-based
cross-domain object recognition tasks, where the proposed method referred to
Domain Adaptation with Virtual Domain Modeling (VDM-DA) achieves the
state-of-the-art performances on all datasets.
