  Unsupervised domain adaptation (DA) methods have focused on achieving maximal
performance through aligning features from source and target domains without
using labeled data in the target domain. Whereas, in the real-world scenario's
it might be feasible to get labels for a small proportion of target data. In
these scenarios, it is important to select maximally-informative samples to
label and find an effective way to combine them with the existing knowledge
from source data. Towards achieving this, we propose S$^3$VAADA which i)
introduces a novel submodular criterion to select a maximally informative
subset to label and ii) enhances a cluster-based DA procedure through novel
improvements to effectively utilize all the available data for improving
generalization on target. Our approach consistently outperforms the competing
state-of-the-art approaches on datasets with varying degrees of domain shifts.
