  There has been a tremendous progress in Domain Adaptation (DA) for visual
recognition tasks. Particularly, open-set DA has gained considerable attention
wherein the target domain contains additional unseen categories. Existing
open-set DA approaches demand access to a labeled source dataset along with
unlabeled target instances. However, this reliance on co-existing source and
target data is highly impractical in scenarios where data-sharing is restricted
due to its proprietary nature or privacy concerns. Addressing this, we
introduce a practical DA paradigm where a source-trained model is used to
facilitate adaptation in the absence of the source dataset in future. To this
end, we formalize knowledge inheritability as a novel concept and propose a
simple yet effective solution to realize inheritable models suitable for the
above practical paradigm. Further, we present an objective way to quantify
inheritability to enable the selection of the most suitable source model for a
given target domain, even in the absence of the source data. We provide
theoretical insights followed by a thorough empirical evaluation demonstrating
state-of-the-art open-set domain adaptation performance.
