  Unsupervised Domain adaptation methods solve the adaptation problem for an
unlabeled target set, assuming that the source dataset is available with all
labels. However, the availability of actual source samples is not always
possible in practical cases. It could be due to memory constraints, privacy
concerns, and challenges in sharing data. This practical scenario creates a
bottleneck in the domain adaptation problem. This paper addresses this
challenging scenario by proposing a domain adaptation technique that does not
need any source data. Instead of the source data, we are only provided with a
classifier that is trained on the source data. Our proposed approach is based
on a generative framework, where the trained classifier is used for generating
samples from the source classes. We learn the joint distribution of data by
using the energy-based modeling of the trained classifier. At the same time, a
new classifier is also adapted for the target domain. We perform various
ablation analysis under different experimental setups and demonstrate that the
proposed approach achieves better results than the baseline models in this
extremely novel scenario.
