  Conventional domain adaptation (DA) techniques aim to improve domain
transferability by learning domain-invariant representations; while
concurrently preserving the task-discriminability knowledge gathered from the
labeled source data. However, the requirement of simultaneous access to labeled
source and unlabeled target renders them unsuitable for the challenging
source-free DA setting. The trivial solution of realizing an effective original
to generic domain mapping improves transferability but degrades task
discriminability. Upon analyzing the hurdles from both theoretical and
empirical standpoints, we derive novel insights to show that a mixup between
original and corresponding translated generic samples enhances the
discriminability-transferability trade-off while duly respecting the
privacy-oriented source-free setting. A simple but effective realization of the
proposed insights on top of the existing source-free DA approaches yields
state-of-the-art performance with faster convergence. Beyond single-source, we
also outperform multi-source prior-arts across both classification and semantic
segmentation benchmarks.
