  We introduce a practical Domain Adaptation (DA) paradigm called
Class-Incremental Domain Adaptation (CIDA). Existing DA methods tackle
domain-shift but are unsuitable for learning novel target-domain classes.
Meanwhile, class-incremental (CI) methods enable learning of new classes in
absence of source training data but fail under a domain-shift without labeled
supervision. In this work, we effectively identify the limitations of these
approaches in the CIDA paradigm. Motivated by theoretical and empirical
observations, we propose an effective method, inspired by prototypical
networks, that enables classification of target samples into both shared and
novel (one-shot) target classes, even under a domain-shift. Our approach yields
superior performance as compared to both DA and CI methods in the CIDA
paradigm.
